Vodafone Group (LSE:VOD) has quietly outperformed the wider market this year, with the share price up about 38% year to date and roughly 40% over the past year.
See our latest analysis for Vodafone Group.
That steady climb, including a roughly 10% 1 month share price return and a 39% 1 year total shareholder return, suggests momentum is building as investors warm to Vodafone Group’s operational reset and earnings recovery potential.
If Vodafone’s rebound has caught your eye, this could be a good moment to broaden your radar and discover fast growing stocks with high insider ownership.
But after such a sharp rerating, is Vodafone Group still trading at a meaningful discount to its long term value, or has the market already moved ahead and priced in the next leg of its recovery?
With the narrative fair value sitting at £0.90 versus a last close of £0.95, the current price leans ahead of those long term assumptions.
The analysts have a consensus price target of £0.858 for Vodafone Group based on their expectations of its future earnings growth, profit margins and other risk factors. However, there is a degree of disagreement amongst analysts, with the most bullish reporting a price target of £1.36, and the most bearish reporting a price target of just £0.6.
Read the complete narrative.
Curious what kind of revenue climb, margin rebuild, and future earnings multiple are needed to back that view? The narrative hinges on bolder assumptions than you might expect.
Result: Fair Value of $0.90 (OVERVALUED)
Have a read of the narrative in full and understand what’s behind the forecasts.
However, Vodafone’s weak German performance and complex restructuring efforts could derail revenue growth and margin rebuild expectations if execution stumbles.
Find out about the key risks to this Vodafone Group narrative.
While the narrative fair value suggests Vodafone Group looks slightly overvalued, its 0.7x price to sales ratio looks far cheaper than both peers at 2.0x and its own 1.5x fair ratio. This hints the market may still be underestimating the turnaround.
See what the numbers say about this price — find out in our valuation breakdown.
LSE:VOD PS Ratio as at Dec 2025
If this perspective does not quite align with your own, or you would rather dig into the numbers yourself, you can craft a personalised view in under three minutes, starting with Do it your way.
A great starting point for your Vodafone Group research is our analysis highlighting 3 key rewards and 2 important warning signs that could impact your investment decision.
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This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.
Companies discussed in this article include VOD.L.
Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team@simplywallst.com
Traders work on the floor of the New York Stock Exchange (NYSE) on December 02, 2025 in New York City.
Spencer Platt | Getty Images
Stock futures are little changed Thursday night as traders await inflation data that could further inform the Federal Reserve’s upcoming interest rate decision.
Futures tied to the Dow Jones Industrial Average added 3 points, or 0.01%. S&P futures and Nasdaq 100 futures were both slightly above the flatline.
In the previous session, the S&P 500 and Nasdaq Composite closed slightly higher, while the Dow Jones Industrial Average ended the day just below the flatline. The tech-heavy Nasdaq closed its eighth positive session in nine, buoyed by a 3.4% gain in Meta shares and a 2.1% gain in Nvidia.
Traders are keeping a close eye on a variety of economic data points, as the November payrolls report is scheduled to come out after the Fed’s Dec. 10 meeting.
Investors earlier digested a report from job placement firm Challenger, Gray & Christmas that showed job cuts in November moved ahead of 1 million for the year, with corporate restructuring, artificial intelligence and tariffs contributing to the losses. Thursday’s release of the latest weekly jobless claims numbers — which showed new applications for unemployment insurance at their lowest level since Sept. 2022 — did not appear to dent sentiment during the trading session.
Investors are hoping that signs of a softening labor market will influence the Fed to lower rates by a quarter percentage point at its next meeting. Traders are pricing in an 87% chance of a cut next Wednesday, far higher than just a couple weeks ago, according to the CME FedWatch tool.
“The data is mixed that we’re getting, and you’re seeing different signals. Inflation is still sticky where it is,” Sonali Basak, iCapital chief investment strategist, said Thursday on CNBC’s “Closing Bell.” “2026 is a wild card as it pertains to inflation. No one has that crystal ball. And you have that with the labor market that has generally held up ‘low hire-low fire.’ If that tips over, then you’re in a pretty sticky spot next year.”
The market will be able to sort through a fresh slate of economic releases on Friday. The Commerce Department will release delayed September data on consumer spending and incomes as well as the personal consumption expenditures index, also considered the Fed’s primary inflation gauge. The PCE report will be the first one since the record-setting U.S. government shutdown. The University of Michigan will also release its consumer survey for December on Friday.
Stocks are managing to eke out slight gains this week. The S&P 500 is up 0.1%, while the Nasdaq and 30-stock Dow have added nearly 0.6% and 0.3%, respectively.
Chris ClementsScotland social affairs correspondent
BBC
Nancy Dunnachie wants to know where her husband’s pension contributions went
Public money will be used to plug gaps in a pension scheme run by an iconic bakery firm that collapsed in 2023.
Former workers at Morton’s Rolls Limited – well-known in the west of Scotland for its crispy morning rolls – had complained that their employer missed payments to the scheme in the run-up to its liquidation.
This was despite deductions being made from their salaries for their pension. BBC Scotland News has seen evidence of gaps in payments to the scheme of up to a year.
A spokesperson for the UK Insolvency Service tells the BBC that National Insurance funds will now be used to pay for the missing contributions.
Nancy Dunnachie, 65, the widow of a former Morton’s Rolls employee tells BBC Scotland: “He kept going on about how they were a ‘shower of rogues’.”
She is talking about her late husband’s former employer as she sifts through a pile of letters from his pension company.
Each letter bears the same heading – ‘payment due to your pension plan’ – and they span a period from 2020 to 2023.
The letters from Standard Life inform William Dunnachie, a former driver for the Glasgow bakery company, that a “payment expected from your employer for [date] was not received for the latest date for payment”.
Mr Dunnachie received multiple letters saying his employer had missed payments
Payments had been missed by Morton’s in various months across the three-year period.
Ms Dunnachie says she has lost count of how many similar letters she has.
She then shows us the payslips from the same period as the letters. They show clear deductions by his employer from William Dunnachie’s earnings for his pension fund.
“He kept getting all these letters in to say that the pension hadn’t been paid,” she says. “But it had been coming off his wages. I think he had asked two or three folk about it, and obviously the boys at work had been talking about it too.
“They [the company] were taking the money off his wages and they weren’t paying it… So where is that money?”
Wullie – to those that knew him – died of a heart attack last October while waiting for redundancy money. He was 66.
At her home in Cumnock, Ayrshire, Ms Dunnachie shows the BBC a redundancy cheque for more than £13,000. It finally arrived last month after a two-year legal battle over who would cover the costs.
She says it is money she can’t yet access due to it being in her husband’s name.
And she still wants answers about his pension deductions made by Morton’s Rolls Limited.
Alan Love worked at Morton’s Rolls for 32 years
The BBC spoke to a number of former Morton’s employees who complained of missing pension payments – money they claim the collapsed company had deducted from their salary but had failed to pass on to Standard Life’s pension scheme.
One worker sent us emails showing how he had been complaining about the issue as early as 2019.
Another is Alan Love, 64, a former driver who served 32 years at Morton’s.
He showed the BBC a statement provided by Standard Life that showed numerous gaps in payments made by Morton’s Rolls Limited. This includes a gap between December 2021 and January 2023.
When asked where his pension payments had gone, he says. “It gets taken off my wages every week, so you tell me.
“For the first couple of years we paid into that scheme, there was never any problem.
“And then, all of a sudden, you’re behind. And then you’re going further behind.
“And then you’re playing catch-up, and then mega catch-up.
“Then the place goes bust and you are two years light on your pension… That isn’t right.”
Alan Love tells the BBC he had contacted the Pensions Regulator (TPR) – a UK body that protects workplace pensions – about the issue before the company went bust.
“It’s not about blowing the lid on anything,” he says. “It’s about getting those payments back, how do I get them back?
“I told them, if this place goes into liquidation, I’ll be playing catch-up.
“And as God is my judge, the only time I didn’t pay into the pension was the week before we went into liquidation.”
A TPR spokesperson said: “We do not comment on individual pension schemes or employers unless appropriate to do so.”
Family handout
William Dunnachie died of a heart attack last October while waiting for redundancy money
William Dunnachie had also worked for Morton’s for 32 years. He was let go in March 2023.
The Drumchapel-based company – well-known across the west of Scotland for its crispy morning rolls – had been suffering financial difficulties before its eventual collapse, with 230 workers being made redundant.
Less than a month later, 110 workers were recalled to work after Morton’s assets were taken over by a new company, Phoenix Volt Limited. A former director of the collapsed company now works for the new company.
Because the former company is in liquidation, there followed a two-year court dispute as 98 workers fought for payouts from the UK government’s Redundancy Payments Service (RPS).
Normally, workers could claim a redundancy payment, but the government argued they were not entitled and said jobs were protected as they had been transferred to the new owner.
Last week, an employment tribunal ruled that workers were entitled to payments as the company was already in liquidation at the time the business was transferred.
The RPS will now pay those funds from the National Insurance Fund.
Paul Kissen, of Thompsons Solicitors, represented the claim.
He estimates his clients could share about £500,000 in redundancy payments.
“There was a level of legitimacy to the government’s challenge because it had to be determined through a complex tribunal process to establish that it was liable,” he says.
“But I think the impact of having to wait so long is unsatisfactory. In my view, if there was a way to expedite this process if there are so many people, that would be the best outcome.”
He is now looking to secure compensation – a “protective award” for Morton’s Rolls failing to consult its employees before redundancy.
Mr Kissen said total payments for his clients could reach £1m – all paid for from the National Insurance Fund.
“Some of these people worked at the company for over 30 years. As a result of the sudden dismissal, they were without any financial means for a long period of time,” he adds.
“Many people managed to secure benefits, many of them didn’t. Some people took very unwell and one of my clients sadly passed away.”
He also told the BBC that some of his clients – William Dunnachie, Alan Love and others – had complained of missing pensions payments.
‘Established process’
A spokesperson for Standard Life tells the BBC there is an “established process” when employer pension contributions are late.
“This was triggered in the case with Morton’s Rolls Limited prior to its insolvency,” he says.
“Employers have until the 21st of the following month to pay outstanding contributions, after which the pension provider initiates a 90-day process to chase contributions.
“If payments are still outstanding at this point, the provider is obliged to inform the Pension Regulator who has a number of enforcement powers to try and pursue contributions with the employer.
“At this point, the pension provider also issues letters to members to inform them about the outstanding contributions.”
In 2023, FRP advisory Trading was appointed administrator of Morton’s Rolls Limited.
The administrator told the BBC that a fresh application was being made to the RPS to cover unpaid pension contributions.
Under the scheme, employees can reclaim contributions deducted from their pay, but not paid into their pension, for the 12 months before the employer became insolvent.
An Insolvency Service spokesperson said: “The Insolvency Service has reviewed the ruling and has decided not to appeal the decision, and the Redundancy Payments Service is currently making payments, including pension payments, to affected employees.”
(Bloomberg) — Asian equities looked set for a weak start after a lackluster session on Wall Street weighed on tech stocks and bonds and saw Bitcoin halt its rebound ahead of next week’s Federal Reserve decision.
Equity index futures for Japan and Hong Kong pointed to declines with stocks in Australia opening lower. US futures were little changed after the S&P 500 climbed 0.1% in the previous session. The yield on 10-year Treasuries rose three basis points to 4.1% on Thursday, the dollar fluctuated and Bitcoin dropped below $93,000.
The muted moves underscore that risk sentiment remained fragile even as the S&P 500 has rebounded in the past two weeks to be within 0.5% of its record closing high. Those gains partly reflected easing concerns over tech valuations and confidence among traders that the Fed will deliver a 25 basis point interest rate cut next week in its last meeting of the year.
Small-caps were a bright spot during the US session though. The Russell 2000 Index climbed 0.8% to a record high, in a sign gains in the stock market are broadening to companies more sensitive to economic growth.
“The key question hanging over markets is whether a potential Federal Reserve rate cut next week can trigger a so-called Santa rally,” said Fawad Razaqzada at Forex.com. “For now, the S&P 500 forecast remains cautiously constructive, albeit with more hesitancy creeping in.”
Bets on a Fed reduction remained intact despite a slide in jobless claims — a noisy reading that captured the Thanksgiving period. Meta Platforms Inc. shares jumped 3.4% after people familiar with the matter said executives are considering budget cuts for the metaverse group.
MSCI Inc.’s gauge for Asian equities rose nearly 1% in the previous session, capping a third straight day of gains. Focus today will be on an interest-rate decision in India and data releases on household spending in Japan, inflation in the Philippines and Taiwan. Markets are closed in Thailand.
US government bonds were sold off on Thursday as data showed signs of resilience in the jobs market. Applications for US unemployment benefits fell last week to the lowest in more than three years, indicating that employers are still largely holding onto workers despite a wave of recent layoffs. Separate data from Challenger, Gray & Christmas showed announced layoffs at US companies fell last month after surging in October, but were still the highest for any November in three years.
“Overall, the net takeaway from the data served to confirm the crosscurrents evident in the labor landscape,” said Ian Lyngen at BMO Capital Markets.
Federal Reserve
Policymakers will not yet have the government’s November jobs report in hand for their meeting next week. The report, originally due Dec. 5, was delayed until Dec. 16 as a result of the record-long government shutdown. That release will also include October payrolls figures.
“There remain some negative payroll employment readings. But the US labor market is not collapsing based on timely data and reports that have leading indicator properties,” said Don Rissmiller at Strategas. “We continue to believe the Fed will cut the fed funds rate again by 25 basis points in December.”
While investors are largely betting policymakers will cut rates again, officials have rarely been so divided as many still prefer leaving rates elevated to keep inflation in check.
Before their meeting, Fed officials will get a dated reading on their preferred inflation gauge. On Friday, the September income and spending report — also delayed because of the government shutdown — is due to be released.
The figures will include the personal consumption expenditures price index and a core measure that excludes food and energy. Economists project a third-straight 0.2% increase in the core index. That would keep the year-over-year figure hovering just below 3%, a sign that inflationary pressures are stable, yet sticky.
“We continue to expect two rate cuts by the end of the first quarter of 2026, with Friday’s personal consumption expenditure index likely to show price pressures under control,” said Ulrike Hoffmann-Burchardi at UBS Global Wealth Management.
Corporate News
Australian data center group NextDC Ltd. and ChatGPT-developer OpenAI agreed to partner on the development of a large-scale data center in Sydney. NextDC’s shares jumped. The cloud-computing startup Fluidstack is in talks to raise roughly $700 million in a funding round that would value the company at $7 billion, according to a person familiar with the situation. Mitsubishi UFJ Financial Group Inc. plans to team up with Morgan Stanley in asset management, deepening their 17-year partnership. Jane Street Group’s record haul this year has been boosted by savvy bets on the artificial intelligence boom that are showing up as big gains in its trading results, according to people familiar with the matter. China’s crackdown on borrowing by local governments is forcing state-run entities in even some of the wealthiest provinces to tap costly credit from non-bank lenders, a stopgap that’s building up risks in an opaque corner of the financial system. Nvidia Corp. would be barred from shipping advanced artificial intelligence chips to China under bipartisan legislation unveiled Thursday in a bid to codify existing US restrictions on exports of advanced semiconductors to the Chinese market. Some of the main moves in markets:
Stocks
S&P 500 futures were little changed as of 8:20 a.m. Tokyo time Hang Seng futures fell 0.2% Australia’s S&P/ASX 200 fell 0.2% Currencies
The Bloomberg Dollar Spot Index was little changed The euro was little changed at $1.1642 The Japanese yen was little changed at 155.16 per dollar The offshore yuan was little changed at 7.0696 per dollar The Australian dollar was little changed at $0.6607 Cryptocurrencies
Bitcoin rose 0.1% to $92,300.2 Ether rose 0.5% to $3,139.49 Bonds
Australia’s 10-year yield declined one basis point to 4.69% Commodities
West Texas Intermediate crude was little changed Spot gold was little changed This story was produced with the assistance of Bloomberg Automation.
Aptar today announced that it has been named one of America’s Most Responsible Companies 2026 by Newsweek for the seventh consecutive year. Aptar is ranked number 56 out of 600 U.S. companies.
“Being recognized by Newsweek for the seventh consecutive year underscores our strong commitment to sustainability as a responsible corporate citizen. Our leadership in sustainable solutions positions us at the forefront of industry transformation, enabling global brands to meet evolving customer and consumer expectations. We continue to invest in renewable energy, certify sites as landfill free, and develop innovative products that are more recyclable, reusable, refillable and incorporate sustainable materials. This is progress that strengthens our competitive advantage and creates long-term value for all stakeholders,” said Stephan B. Tanda, Aptar President and CEO.
In addition to the recognition by Newsweek, Aptar was recently recognized by Forbes as a World’s Top Companies for Women, one of the World’s Most Sustainable Companies by TIME and was also named to the CDP A-list for the fourth consecutive year.
Aptar actively strives to create new opportunities through product innovation while respecting the planet. As an active member of the Ellen MacArthur Foundation and the World Business Council for Sustainable Development (WBSCD) the company is working alongside other leaders to further actions towards a more circular economy. Aptar publishes an annual Sustainability Report and GRI Index to record and highlight its sustainability efforts.
Newsweek, in partnership with Statista, evaluated the top 2,000 largest public companies with U.S. headquarters by revenue, based on publicly available data encompassing the three pillars of ESG (Environment, Social and Governance) and a company perception study of 26,000 individuals. Both the survey and analysis evaluated over 30 key performance indicators (KPIs), including emissions, energy use, board diversity, as well as disclosure and transparency. The ranking represents the 600 U.S. companies with the highest overall CSR scores, across 14 industries.
The full list of America’s Most Responsible Companies 2026 by Newsweek can be found here.
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A US private equity firm has agreed to postpone a disputed deal to sell its stake in a large gas driller to one of its own funds, after a large Middle Eastern sovereign wealth fund sued to block the transaction.
Houston-based Energy & Minerals Group has consented to the delay of the planned sale of a 30 per cent stake in Ascent Resources, one of the largest private natural gas drillers in the US, to one of its sister funds after the Abu Dhabi Investment Council alleged the deal short-changed investors.
The lawsuit, reported exclusively by the Financial Times on Wednesday, asked for an emergency halt to EMG’s planned sale of its Ascent stake to a new so-called continuation fund managed by the PE group by the end of the year. The two parties agreed on Thursday morning to delay the transaction until at least late February, after it was vetted by a commercial arbiter, they said in a motion approved by a Delaware court.
The dispute between the sovereign wealth fund, which is part of the $300bn investing giant Mubadala and EMG, an energy investor founded by John Raymond, son of longtime Exxon chief executive Lee Raymond, shone a further spotlight on the potential conflicts that can arise as PE groups resort to selling assets between funds to exit ageing investments.
Abu Dhabi Investment Council accused EMG of forcing a “conflicted sale” of Ascent Resources at too low a valuation in an effort to “reap a massive benefit for themselves at the expense of ADIC and the other investors”.
The sovereign wealth fund objected to the price EMG was offering for Ascent Resources, which holds vast gas reserves in Ohio’s Utica shale. It also accused the PE group of using the fund-to-fund sale to reset performance fees on a deal it said was unlikely to generate lucrative “carried interest” fees, if it were sold to an independent buyer or taken public.
The lawsuit comes as many private equity investors and advisers have warned about the fraught nature of fund-to-fund deals, with PE groups acting as both the seller and the buyer in the transactions. Such deals have soared in popularity as private equity groups have struggled to find buyers for trillions of dollars in unsold assets. Continuation deals amounted to a record 19 per cent of all PE asset sales in the first half of 2025, the FT previously reported.
In addition to asking for an injunction against its continuation fund deal, the Abu Dhabi-based sovereign wealth fund is calling for EMG to run a full sale process for Ascent Resources, which it believes would have interest from strategic buyers or could be taken public.
The sovereign fund declined to comment on ongoing litigation. EMG and its lawyers did not respond to emails seeking comment.
Chinese hackers target critical infrastructure with “Brickstorm” malware
China denies involvement, calls accusations “irresponsible”
US, Canadian governments share details on Brickstorm
Dec 4 (Reuters) – Chinese-linked hackers used sophisticated malware to penetrate and maintain long-term access to unnamed government and information technology entities, U.S. and Canadian cybersecurity agencies said on Thursday.
The Chinese-linked hacking operations are the latest example of Chinese hackers targeting critical infrastructure, infiltrating sensitive networks and “embedding themselves to enable long-term access, disruption, and potential sabotage,” Madhu Gottumukkala, the acting director of the Cybersecurity and Infrastructure Security Agency, said in an advisory, opens new tab signed by CISA, the National Security Agency and the Canadian Centre for Cyber Security.
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Liu Pengyu, a spokesperson for the Chinese embassy in Washington, said in an email that the Chinese government does not “encourage, support or connive at cyber attacks,” and that “we reject the relevant parties’ irresponsible assertion” about the activities in question, when the parties had “neither put forward any request related to the issue nor presented any factual evidence.”
Chinese-linked hackers have been targeting a host of U.S. and global telecommunications companies and other sensitive targets in recent years, according to U.S. government warnings. In October, sources linked a hack targeting U.S. cybersecurity company F5 to Chinese-linked hackers.
According to the advisory, which was published alongside a more detailed malware analysis report, opens new tab, the state-backed hackers are using malware known as “Brickstorm” to target multiple government services and information technology entities. Once inside victim networks, the hackers can steal login credentials and other sensitive information and potentially take full control of targeted computers.
In one case, the attackers used Brickstorm to penetrate a company in April 2024 and maintained access through at least September 3, 2025, according to the advisory. CISA Executive Assistant Director for Cybersecurity Nick Andersen declined to share details about the total number of government organizations targeted or specifics around what the hackers did once they penetrated their targets during a call with reporters on Thursday.
The advisory and malware analysis reports are based on eight Brickstorm samples obtained from targeted organizations, according to CISA. The hackers are deploying the malware against VMware vSphere, a product sold by Broadcom’s (AVGO.O), opens new tab VMware to create and manage virtual machines within networks.
A Broadcom spokesperson said in an email that the company was aware of reports of hackers using Brickstorm “after obtaining access to customer environments.” The company encourages all customers to apply up-to-date software patches and adhere to strong operational security, the spokesperson said.
In September, Google’s Threat Intelligence Group reported responding to Brickstorm-linked intrusions across a range of industries, including legal services, software service providers, business process outsourcers and technology.
In addition to traditional espionage, the hackers in those cases likely also used the operations to develop new, previously unknown vulnerabilities and establish pivot points to broader access to more victims, Google said at the time.
Reporting by AJ Vicens in Detroit; Editing by Matthew Lewis
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Chinese artificial intelligence company DeepSeek has released a mathematical reasoning model that can identify and correct its own errors. The model beat the best human score in one of the world’s most prestigious undergraduate maths competitions.
The model, DeepSeekMath-V2, scored 118 out of 120 points on questions from the 2024 William Lowell Putnam Mathematical Competition, beating the top human score of 90. The model also performed at the level of gold-medal winners in the International Mathematical Olympiad (IMO) 2025 and the 2024 China Mathematical Olympiad. The results are described in a preprint1 posted on arXiv on 27 November.
“We are at a point where AI is about as good at maths as a smart undergraduate student,” says Kevin Buzzard, a mathematician at Imperial College London. “It is very exciting.”
In February, AlphaGeometry 2, an AI problem solver created by Google DeepMind in London, also achieved a gold-level performance in the IMO. The feat was repeated in July by Gemini’s Deep Think, which is owned by DeepMind.
Reasoning over answers
Early approaches to training large language models for mathematical reasoning focused on the accuracy of final answers, the preprint authors write. But a correct answer does not guarantee correct reasoning. At times, a correct final answer might just be a result of a fortunate error. Moreover, an exclusive focus on the end result is not useful in proving mathematical laws or formulae, when the logical reasoning is more important than the final answer.
Tong Xie, a chemist specialising in AI-driven discoveries at UNSW Sydney in Australia, says the researchers behind DeepSeek, as well as those developing Gemini’s Deep Think, have been working on overcoming this problem by rewarding reasoning over the final answer.
DeepSeekMath-V2 introduces self-verifiable mathematical reasoning for the first time. The model consists of a verifier trained to evaluate mathematical proofs — which are built on a series of step-by-step deductions — to identify logical flaws and assign scores based on how rigorous the proof was. A meta-verification system then checks whether the verifier’s critiques are accurate, reducing the likelihood of hallucinations and improving trustworthiness. These components work with a proof generator that constructs solutions and evaluates its own work, refining arguments until no further issues can be found.
The design creates a feedback loop: the verifier improves the generator, and as the generator produces more-challenging proofs, these become new training data to strengthen the verifier.
The system was able to solve five out of six problems, scoring 83.3%, in the 2025 IMO. It was, however, unable to solve the hardest problems set in 2025 and in past IMOs.
Math-V2 relies on self-verification using natural language in the model itself, Xie says. This reduces human involvement and makes the model more cost-effective and scalable.
Gemini’s Deep Think, by contrast, verifies mathematical reasoning using an external, symbolic language called Lean, and its verification process requires extensive expert input. The method is nearly free of hallucination, but it is computationally expensive and resource-intensive, Xie says.
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 Ɵk. 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.
Figure 1. CONSORT (Consolidated Standards of Reporting Trials) flow diagram.
Table 1. Sample characteristics (N=60).
Sample characteristic
Assessment (n=30), n (%)
Feedback (n=30), n (%)
Total (n=60), n (%)
Gender
Man
16 (53.3)
14 (46.6)
30 (50)
Woman
14 (46.7)
15 (50)
29 (48.3)
Nonbinary
0 (0)
1 (3.3)
1 (1.6)
Race
Asian
2 (6.6)
1 (3.3)
3 (5)
Black
3 (10)
3 (10)
6 (10)
Multiracial
0 (0)
1 (3.3)
1 (1.6)
Other
1 (3.3)
0 (0)
1(1.6)
White
24 (80)
25 (83.3)
49 (81.6)
Ethnicity
Not Hispanic or Latine
25 (83.3)
26 (86.6)
51 (85)
Hispanic or Latine
5 (16.6)
4 (13.3)
9 (15)
Student status
Student
19 (63.3)
21 (70)
40 (66.6)
Nonstudent
11 (36.6)
9 (30)
20 (33.3)
Employment status
Part-time
8 (26.7)
16 (53.3)
24 (40)
Not working
12 (40)
7 (23.3)
19 (31.7)
Full-time
10 (33.3)
7 (23.3)
17 (28.3)
Met criteria for AUD
No
7 (23.3)
8 (26.7)
15 (25)
Mild
14 (46.7)
9 (30)
25 (41.7)
Moderate
7 (23.3)
9 (30)
16 (26.7)
Severe
2 (6.7)
4 (13.3)
6 (10)
Met criteria for other SUD
No
24 (80)
23 (76.7)
47 (78.3)
Yes
6 (20)
7 (23.3)
13 (21.7)
Ever AUD or SUD treatment
No
30 (100)
29 (96.6)
59 (98.3)
Yes
0 (0)
1 (3.3)
1 (1.6)
Met criteria for a MHD
No
25 (83.3)
26 (86.7)
51 (85)
Yes
5 (16.6)
4 (13.3)
9 (15)
aAge=mean 22.02, SD 1.98, range 18.03‐25.94 years.
b“Gender” refers to participants’ self-identified gender identity, not biological sex.
cAUD: alcohol use disorder.
dBaseline alcohol use (past 28 d): Total standard alcoholic drinks=mean 73.91, SD 36.89; range 28.50‐219.49 drinks. Alcohol grams/day=mean 36.96, SD 18.45; range 14.25‐109.75 grams/day.
eSUD: substance use disorder.
fMHD: mental health disorder.
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.
Table 2. Exit survey results: rated agreement on a scale (0-100; n=60).
Mean (SD), (1‐100)
Rated positive (>50), n (%)
Number
Acceptability
Feedback report sleep/cardio information was likeable
92.67 (13.25)
56 (96.6)
58
Were willing to wear Oura Ring another week
90.83 (17.20)
55 (91.7)
60
Were willing to wear Oura Ring in future
89.17 (18.62)
55 (91.7)
60
Feedback report alcohol or substance info was likeable
87.50 (15.55)
30 (93.8)
32
Were willing to use Oura Ring with app in future
86.25 (20.80)
53 (88.3)
60
Oura Ring was not embarrassing to wear
84.58 (17.28)
57 (95)
60
Overall program was satisfying
84.17 (17.81)
52 (86.7)
60
Graphics in feedback report were acceptable
83.19 (17.76)
50 (86.2)
58
The quality of feedback report info was acceptable
82.33 (16.89)
53 (91.4)
58
Feedback report was interesting
76.72 (18.65)
46 (79.3)
58
Feedback report was visually appealing
76.29 (22.17)
43 (74.1)
58
Oura Ring was likeable
76.25 (22.75)
44 (73.3)
60
Feedback report layout was acceptable
75.00 (24.33)
43 (74.1)
58
Smartphone diary was likeable
59.17 (25.20)
26 (43.3)
60
Feasibility
Oura Ring was not itchy
93.52 (13.77)
58 (96.7)
60
Oura Ring did not interfere with concentration
92.08 (14.18)
59 (98.3)
60
Overall program visits did not interfere with schedule
91.67 (15.03)
58 (96.7)
60
Oura Ring did not interfere with sleep
91.25 (16.48)
58 (96.7)
60
Oura Ring did not irritate skin
90.93 (15.38)
59 (98.3)
60
Overall program visits were comfortable
90.00 (14.70)
57 (95)
60
Oura Ring stayed on finger
87.92 (19.79)
55 (91.7)
60
Oura Ring did not interfere with activities
86.25 (18.65)
55 (91.7)
60
Oura Ring did not result in sweatiness
86.11 (18.14)
57 (95)
60
Feedback report alcohol/substance info was understandable
84.38 (18.78)
27 (84.4)
32
Smartphone diary was easy to complete
82.50 (20.74)
52 (86.7)
60
Oura Ring did not interfere with accessories
81.67 (24.30)
48 (80)
60
Oura Ring did not interfere with schedule
81.25 (28.61)
51 (85)
60
Smartphone diary did not interfere with schedule
80.83 (26.98)
49 (81.7)
60
The quantity of feedback report info was right
80.17 (17.99)
48 (82.8)
58
Remembered to wear and charge the Oura Ring
79.17 (23.55)
49 (81.7)
60
Feedback report sleep/cardio info was understandable
78.88 (21.87)
44 (75.9)
58
Overall program visits were not too long
77.92 (25.25)
46 (76.7)
60
Able to forget Oura Ring while wearing
76.67 (26.39)
47 (78.3)
60
Feedback report was understandable
70.26 (20.12)
37 (63.8)
58
Oura Ring did not interfere with exercise
70.00 (33.13)
39 (65)
60
Oura Ring was comfortable
68.33 (29.06)
42 (70)
60
Remembered to complete smartphone diary
65.42 (28.03)
39 (65)
60
Were willing to complete smartphone diary in an app
64.17 (28.51)
33 (55)
60
Smartphone diary was not burdensome
60.00 (27.69)
29 (48.3)
60
Perceived effectiveness
Feedback report sleep/cardio information was helpful
88.36 (17.66)
53 (91.4)
58
Feedback report sleep tips were helpful
85.00 (18.10)
26 (86.7)
30
Feedback report alcohol/substance info was helpful
84.48 (18.03)
50 (86.2)
58
Feedback report alcohol use tips were helpful
81.90 (21.02)
22 (75.9)
29
Feedback report exercise tips were helpful
76.61 (21.35)
21 (67.7)
31
Overall program is effective in meeting its goals
75.83 (21.57)
49 (81.7)
60
Feedback report stress-related tips were helpful
73.28 (28.29)
21 (72.4)
29
Feedback report diet tips were helpful
72.50 (25.72)
20 (66.7)
30
Overall program supports lifestyle goals
70.83 (20.15)
37 (61.7)
60
Overall program promotes hope
70.42 (25.00)
35 (58.3)
60
Feedback report substance use tips were helpful
69.64 (24.87)
16 (57.1)
28
Oura Ring app readiness tips were helpful
67.00 (29.51)
14 (56)
25
Oura Ring app bedtime tips were helpful
56.25 (34.77)
11 (45.8)
24
Oura Ring app activity prompts were helpful
52.88 (30.27)
9 (34.6)
26
Other
Habits targeted by overall program were important
82.08 (20.11)
49 (81.7)
60
Oura Ring app use frequency (weekly to multiple daily)
83.33 (21.71)
28 (93.3)
30
Were willing to purchase Oura Ring
49.35 (29.10)
23 (45.1)
51
Were willing to purchase feedback report tips
43.96 (26.01)
19 (35.8)
53
aMean (SD) are based on participants’ ratings of agreement with each statement on acceptability, feasibility, or perceived effectiveness on a 0‐100 scale. All values over 50 correspond to agreement with the statement (positive ratings of intervention features), and values over 75 indicate strong agreement. Not all participants were asked each question. Only feedback group participants answered questions about the Oura Ring app, and some questions were added later in the study and only answered by some participants. Calculations are based on the subset of participants who were asked each question, and no imputation methods were used with missing data. “Yes/No” exit survey items (eg, whether participants read all feedback reports) are reported in text.
b83.3% (50/60) of participants described the overall program’s goals in part as learning about alcohol, alcohol’s relationship to sleep, and/or developing healthier drinking habits.
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 (Ɵk=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 (Ɵk=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 (Ɵk=0.17), or multiple helpful program components specifically promoting sleep improvement and alcohol reduction, and mindful sleep strategies (Ɵk=0.17), or trying mindfulness to improve sleep. These were followed by awareness before change (Ɵk=0.15; or planning health changes based on personalized feedback reports) and multimodal sleep or caffeine insights (Ɵk=0.15; or learning about sleep or caffeine from multiple helpful aspects of the program.
Figure 2. Feedback group exit interviews intertopic distance map (n=29). This map shows the distance between 6 topics within the Latent Dirichlet Allocation model based on pairwise Jensen–Shannon divergences between topic-word distributions. These were embedded in 2D using classical multidimensional scaling. Topics closer together on the map are more semantically similar. Larger point size and darker color indicate higher prevalence of a topic across exit interviews (Ɵk). In order of prevalence, topic names based on recursive reading of interviews are: multimodal general change (Ɵk=0.18); learning+peer coach interest (Ɵk=0.18); multimodal sleep, alcohol change (Ɵk=0.17); mindful sleep strategies (Ɵk=0.17); awareness before change (Ɵk=0.15); and multimodal sleep, caffeine insights (Ɵk=0.15). The legend shows the 10 highest-probability terms within each topic. alc: alcohol; MDS: multidimensional scaling.
We modeled 5 primary topics from the assessment exit interviews (n=21; ). The most common topic was multimodal insights, good sleep (Ɵk=0.22), or learning about good sleep from multiple helpful components of the program. Next most common topics were self-guided report use (Ɵk=0.20; or learning from the feedback report without a coach) and report insights, poor sleep (Ɵk=0.20; or learning about sleep deficits from the feedback report). These were followed by continued multimodal mobile health use (Ɵk=0.19), or finding multiple aspects of the program helpful due to previous use of mobile health, and multimodal sleep strategies (Ɵk=0.18), or trying sleep strategies based on multiple helpful components of the program.
Figure 3. Assessment group exit interviews intertopic distance map (n=21). This map shows the distance between 5 topics within the Latent Dirichlet Allocation model based on pairwise Jensen–Shannon divergences between topic-word distributions. These were embedded in 2D using classical multidimensional scaling. Topics closer together on the map are more semantically similar. Larger point size and darker color indicate higher prevalence of a topic across exit interviews. In order of prevalence, topic names based on recursive reading of interviews are: multimodal insights, good sleep (Ɵk=0.22), self-guided report use (Ɵk=0.20); report insights, poor sleep (Ɵk=0.20); continued multimodal mHealth use (Ɵk=0.19); and multimodal sleep strategies (Ɵk=0.18). MDS: multidimensional scaling; mHealth: mobile health.
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%).
Figure 4. This histogram shows the frequency of sentiment scores (mean 14.66, SD 7.53; range −4 to 30) in the exit interviews of feedback group participants (n=29). These scores were calculated using the AFINN lexicon []. 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 group (28/29, 96.6%) had positive sentiment scores (>0), and 86.2% (25/29) had high positive sentiment (>10). AFINN: Finn Årup Nielsen. Figure 5. This histogram shows the frequency of sentiment scores (mean 15.57, SD 9.65; range 1‐32) in the exit interviews of assessment group participants (n=21). These scores were calculated using the AFINN lexicon []. Positive sentiment scores indicate overall positively valenced words within an exit interview, whereas negative sentiment scores indicate negatively valenced words. All assessment participants (21/21, 100%) had positive sentiment scores (>0), and 71.4% (15/21) had high positive sentiment (>10). AFINN: Finn Årup Nielsen.
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.
Table 3. Convergent mixed methods results (asterisks denote findings that converged across methods).
Intervention
Exit survey
Exit interview
NLP (topics+sentiment)
Researcher-coded rapid qualitative analysis
Overall program
*High acceptability, feasibility, and effectiveness
Assessment group feasibility > feedback group
*High acceptability
*Effectiveness
Feedback group effectiveness > assessment group
Oura Ring or app
High acceptability and feasibility (Ring)
*Moderate effectiveness (app)
—
*High effectiveness
Feedback group: most influential, learned more about sleep
Diaries
Moderate acceptability and moderate to high feasibility
—
Feedback report
*High feasibility, effectiveness
High acceptability
*Learning about sleep, especially deficits
*Assessment group: prefer self-guided
*High feasibility, moderate to high effectiveness
*Learning about sleep, especially deficits
*Assessment group: prefer self-guided or peer-guided
Feedback group: prefer coach, clinician
All prefer remote coaching
aNLP: natural language processing.
bNot applicable.
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.
The authors would like to acknowledge Holly Boyle and Sophia Sniffin for their participation in the rapid qualitative analysis process. ChatGPT-5 from OpenAI was used for the revision of the R code used to create “Figures 1–2”.
This research was directly supported by a grant from the National Institute on Alcohol Abuse and Alcoholism under award number R21AA028886. Additional grants from the National Institutes of Health that supported effort are as follows: T32DA019426 (FJG), T32DA007238 (OKE), K01DK129441 (GIA), and R01AA030136.
The datasets used and analyzed during this study are available from the corresponding author on reasonable request. The underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author.
The authors attest that no external sponsors had influence on the design of this study, its outcomes, or the decision to publish.
FJG is an unpaid consultant for Calm Health.
KSD reports a provisional patent file for a digital system for lifestyle medicine (47162-5346-P1US), and registration of the name and content of the Call it a Night web-based sleep program with the U.S. Patent and Trademark Office (since expired).
SSO reports being a member of the American Society of Clinical Psychopharmacology’s (ASCP) Alcohol Clinical Trials Initiative, supported by Alkermes, Dicerna, Eli Lilly and Company, Ethypharm, Indivior, Imbrium Therapeutics, Osuka, Pear Therapeutics, and Kinnov Therapeutics; consultant/advisory board member, Dicerna, Eli Lilly and Company, Newleos Therapeutics, University of New Mexico (NIH grant); stock options, Newleos Therapeutics; medication supplies, Novartis/Stalicla, Amygdala; contracts, Tempero Bio, Altimmune; DSMB member, Emmes Corporation, Indiana University; patent application on mavoglurant for gambling disorder with Novartis and Yale; and grants from the NIH and FDA.
GIA is a scientific advisor to Behavioral Health Tech Innovations LLC. GIA receives professional services from Calm.com (nominal fee), Labfront (full fee), and GlucoseZone (full fee). GIA reports a provisional patent filed for a digital system for lifestyle medicine (047162–5346-P1US). GIA in the past 5 years has been supported by a VHA Office of Academic Affiliations Fellowship, Robert E. Leet and Clara Guthrie Patterson Trust Mentored Research Award, Bank of America, N.A., Trustee, and American Heart Association Grant #852679 (2021-2024).
LMF reports grant funding from the US National Institutes of Health to directly support the research (R21AA028886), a provisional patent file for a digital system for lifestyle medicine (47162-5346-P1US), registration of the name and content of the Call it a Night web-based sleep program with the U.S. Patent and Trademark Office (since expired), and paid consultation for serving on an advisory board for Imbrium Therapeutics. All other authors report no disclosures.
Edited by Alicia Stone; submitted 05.Jun.2025; peer-reviewed by Liam Allan, Richard Cooke; accepted 22.Oct.2025; published 04.Dec.2025.
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