Survey: 45% Report Health App Burnout as Average User Juggles Six Apps

Nearly half of Americans are feeling overwhelmed by the number of digital health tools they have, and many report health app burnout, according to new research.

A survey of 2,000 insured adults aged 18-65 found that the average person uses six different health-related apps on a regular basis — with one in five having upward of 10 (22%).

Image: HUUM / Unsplash

For some, that includes daily activity trackers (57%), nutrition apps (39%) and sleep tracking tools (37%), while others utilize health apps for ongoing care needs like weight management support (34%) and virtual care to connect with doctors (30%).

While nearly one quarter (23%) use apps to manage a specific chronic health condition, more than one in 10 (14%) respondents admit they use these health tools to try popular health trends they’ve seen online.

On average, respondents spend over an hour every week manually logging their data and checking their health apps at least once a day (58%). In fact, more than one in ten (11%) admit to checking their app data hourly.

As a result, eight in 10 Americans said their phone now knows their health better than they themselves do (79%).

Even though the data shows that Americans love tracking their health via apps, the survey conducted by Talker Research for MD Live found there are certain drawbacks.

More than half (53%) feel there are too many health apps to keep track of, and 45% say they’ve felt “burnt out” on a weekly basis just from trying to stay on top of inputting information into their apps. More than one in ten (15%) feel exhausted trying to keep up with alerts.

A third of those surveyed have downloaded apps that they didn’t end up using (32%), so it’s no surprise that 24% have deleted at least four of them over the past two years.

Respondents shared that their disinterest grew when these apps required a subscription (27%) or displayed too many ads or tried to push products (23%). Nearly one in five (17%) have deleted an app because they say they have received conflicting or confusing information.

On top of that, 40% admit they don’t know how to best use these apps to their advantage and 41% note that they often feel like they’re juggling too many.

As a result, one quarter say they have forgotten to follow through on a health goal or appointment because they were managing too many tools.

“People aren’t overwhelmed by technology, they’re overwhelmed by the number of choices,” said Dr. Maggie Williams, medical director for Primary Care at MD Live by Evernorth. “Most consumers want to engage in their health and find digital tools useful. They just want help understanding which tools are right for them and how to get the most out of them.”

Even so, many Americans aren’t giving up on digital health. Forty-one percent plan to use more health tools and apps in 2026, especially for fitness or activity tracking (54%), weight loss or management support (50%) and nutrition tracking (49%).

Despite the effort that goes into maintaining these apps, the payoff is worth it for many.

Nine in 10 said health tools have improved their understanding of how their body works (91%) and have inspired them to feel motivated (38%), in control (36%) and confident about the decisions they make (33%).

Respondents say they gain value from learning more about themselves, such as identifying personal patterns (34%) and better understanding their body’s needs (31%). For some, it also helps them stay motivated (37%) and improves their mindfulness (28%).

Even with these benefits, consumers still need help wading through it all. Nearly two-thirds of those surveyed want more help from a healthcare provider in deciding what health tools and apps are right for them (62%), and 54% want more communication from their health plan about the tools available to them.

Respondents dished on what would make them use health tools/apps more efficiently and reported that the top priority would be all their apps and tools living together in one place (28%), followed closely by all their apps being synced to share data (27%).

Those polled were also asked what they’d include in their idea of the “perfect health app,” and a sleep tracker scored the highest (37%). That was followed by an activity tracker (31%), a heart rate monitor (31%), step counter (30%), blood pressure monitor (30%) and stress tracker (30%).

“It’s hard to know which tools are truly right for you,” said Dr. Williams. “Your doctor can help you prioritize your needs and narrow the choices, and some health plans now offer recommended app lists tailored to different health needs. Both can help make the digital health world much easier to navigate.”

Reviewed by Irfan Ahmad.

This post was originally published by Talker Research and is republished here in accordance with their republishing guidelines.

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Chatbots overemphasize sociodemographic stereotypes, researchers report

By Mary Fetzer

People interact with artificial intelligence (AI)-powered chatbots, which can be trained to take on certain demographic attributes like age and race, for information, entertainment, technical help, learning, emotional support and more. But how realistically do these AI personas mimic real people? For some demographics, not well, according to researchers at Penn State's College of Information Sciences and Technology (IST).

The researchers found that chatbots relied on superficial stereotypes and exaggerated cultural markers that diminish the authentic experiences of the humans they’re meant to represent. The team presented their findings at the 40th Annual Conference of the Association for the Advancement of Artificial Intelligence (AAAI), which was held Jan. 20-27 in Singapore. The presentation was part of a special track on AI alignment — the idea that AI systems should best represent the values humans think are important, ethical and fair.

The research was led by Shomir Wilson, an associate professor in the College of IST’s Department of Human-Centered Computing and Social Informatics and director of the Human Language Technologies Lab at Penn State, and Sarah Rajtmajer, an associate professor in the College of IST’s Department of Informatics and Intelligent Systems and a research associate in the Rock Ethics Institute.

“We conducted this research under the hypothesis that we’ll increasingly encounter more persona-like chatbots as AI becomes more integrated into our lives,” Wilson said. “Users may be more willing to interact with chatbots that represent a particular background, but we found that current bots don’t represent people from some backgrounds well.”

Large language models (LLMs) are a type of AI used to construct chatbots. The researchers told LLMs — including GPT-4o, Gemini 1.5 Prio and DeepSeek v2.5 — to take on personas based on factors such as age, gender, race, occupation, nationality and relationship status. They asked more than 1,500 AI-generated personas about their lives — such as “Please describe yourself. What are your most defining traits or qualities? What skills do you excel at?” — and compared their responses to those of real people with similar sociodemographic characteristics. They found that the LLMs produced stereotypical written language often used to describe minoritized groups — and did so more than their human counterparts.

Image: Saradasish Pradhan / Unsplash

“The study showed that while chatbots often appear human-like, they overemphasize racial markers and flatten complex identities into stereotypes,” Wilson said. “The AI-generated personas rely on patterns that signal specific cultural assumptions rather than reflecting authentic lived experiences.”

For example, when questions were asked of a chatbot trained to represent a 50-year-old African American woman, the bot talked about gospel music, tough love, social justice, natural hair care and other stereotypical topics that differ from what real people of that demographic would say. While a person might touch on one or two such topics, human responses to the same questions generally don’t include all of them. Instead, the 141 real people surveyed by the researchers talked about more individualized things like work, parenting, volunteering and their health.

The chatbots appeared to be providing answers that were complex and well-structured, but in reality, they were using culturally coded language to oversimplify the experiences of the minority communities they were trained to represent, Wilson said.

The researchers observed four types of representational harm:

• Stereotyping — relying on generalizations and conventional tropes regarding specific racial or cultural groups
• Exoticism — positioning minoritized identities as foreign, other or exotic to enhance the narrative
• Erasure — flattening or omitting complex histories and individualities that define real-world identities
• Benevolent bias — using language that bypasses bias filters by being polite or positive

“LLMs are increasingly used in high-stakes settings — for example, as chatbot companions or as simulated human subjects in scientific research,” Rajtmajer said. “In this study, we show that current LLMs magnify harmful stereotypes in a racist way, which should give pause to developers seeking to integrate personas in real-world applications. These tendencies shouldn’t be buried in the new technologies being developed and released into the world.”

According to the researchers, this work diagnosed a problem that needs to be treated during the development stage.

“Our study highlights how AI-generated content may seem human but can mask deep representational bias,” Wilson said. “What’s needed are design guidelines and new evaluation metrics to ensure ethical and community-centered persona generation.”

This includes a transition from simple word-level detection to more sophisticated auditing that can assess the context and narrative depth of identity representation, Wilson explained. It also involves engagement between the developers creating these personas and the communities they intend to represent.

“A community-centered validation protocol can help ensure that AI-generated personas resonate with actual lived experiences,” Wilson said.

Jiayi Li and Yingfan Zhou, graduate students pursuing doctoral degrees in informatics from the College of IST, also contributed to this research. Pranav Narayanan Venkit, who earned his doctorate in informatics from IST in 2025, was first author on the AAAI paper, titled, “A Tale of Two Identities: An Ethical Audit of Human and AI-Crafted Personas.”

The U.S. National Science Foundation supported this work.

Note: This post was originally published by The Pennsylvania State University and is republished with permission on DIW.

Reviewed by Irfan Ahmad.

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Ensuring Smartphones Have Not Been Tampered With

With increasing cyberattacks and government data breaches, one of the most important devices to keep secure is the one in everyone’s pocket: smartphones. The problem is that it is difficult to check that a smartphone has not been tampered with without the risk of unintentionally damaging the device itself.

In AIP Advances, by AIP Publishing, researchers from the University of Colorado Boulder and the National Institute of Standards and Technology developed a way to remotely fingerprint and identify a cellular device. Their method can help ensure a phone has not been altered during its manufacturing process, reducing the risk of espionage.

When smartphones communicate with a cell tower, they emit a set of electromagnetic waves. Using specialized SIM cards and cellular radio standards-compliant base station emulator equipment, the researchers commanded a set of “trusted” cell phones — devices they know have not been modified — to transmit the exact same sets of signals, allowing them to create a database of what these signals really look like for different phone models, which serve as fingerprints of the model.

“Think of it like giving every phone the exact same song to sing. Even though they are singing the same notes, every phone model has tiny, microscopic differences in its internal hardware,” said author Améya Ramadurgakar. “Our system is sensitive enough to hear those subtle ‘vocal’ differences.”

By comparing the signals emitted by an unknown device to the database, the researchers can figure out if the device has been altered — that is, if its signals do not match up with any of the trusted fingerprints.

They tested this process on multiple commercially available, current-generation smartphones from all major manufacturers currently leading the domestic market to over 95% accuracy. These results were both repeatable and stable over time. Because their method focuses on the fundamental electromagnetic behavior of the hardware, it is not limited to current 4G and 5G mobile networks and will be extendable to future generations of cellular technologies.

Ramadurgakar said this method lays the groundwork for the National Metrological Institute’s testing framework. To formalize this solution, the researchers need to expand their library of trusted sources that accounts for potential small variations between manufacturing batches, develop standardized test conditions, and develop a more automated process.

“This work demonstrates a foundational approach to obtaining a high-definition, reliable, and stable fingerprint of a commercially available smartphone device to verify that it has not been tampered with or compromised prior to deployment,” said Ramadurgakar. “I see this being utilized to validate mobile hardware before it is issued to high-security users, such as the military chain of command or senior government leadership.”

Image: Alicia Christin Gerald / Unsplash

This post was originally published on AIP and is republished here with permission.

Reviewed by Asim BN.

Read next: Do Gig App Fees Vary Across Different Types of Work?

by Press Releases via Digital Information World

Do Gig App Fees Vary Across Different Types of Work?

Gig work has become a defining feature of the labor market in 2025. It’s believed that anywhere from 25% to 43% of the workforce participates in gig work, and at least one in ten rely on it for their primary income.

Traditionally referred to as freelance or contingent work, this type of employment has exploded in popularity over the last ten years, thanks to a number of factors, such as the times of widespread stay-at-home measures and the emerging popularity of service apps like Uber and DoorDash. Many workers are drawn to these jobs for their convenience and flexibility, but behind the apparent accessibility of these platforms is a confusing and often opaque system of fees.

Recently, LLCAttorney created an in-depth comparison of the various gig apps popular in the market today and the data shows these fees are by no means universal. Some apps take almost nothing from each transaction, while others can claim a substantial share of a worker’s earnings. Ranging from 0% all the way up to 50%, at first, these inconsistencies may seem random, but upon closer inspection, a pattern emerges. The differences in how and the rate at which gig apps charge workers shows that these companies have a different fee for selling your skills, selling your time, selling wares, and selling your trustworthiness.

Selling Skills

When selling a specific skill, such as graphic design, coding, or writing, the gig platform’s role is typically that of an intermediary or middle-man, rather than a manager of the work itself. For this reason, many of these platforms charge a sort of “finder’s fee.”

For example, Fiverr, a popular app for graphic designers, video editors, and more, charges a flat 20% fee on every transaction. Freelancer.com operates similarly, taking either 10% or $5, depending on which sum is greater. These platforms operate like digital matchmakers, charging for access to clients.

Some freelance platforms have a sliding fee model, such as Upwork and 99Designs. Upwork’s fees range from 0% to 15%, depending on the industry, whereas 99Designs’ creators get 5% to 15% depending on their skill level, meaning workers with more niche skills may be charged less by the platform.

• Also read: The Real Price of Digital Clutter in the Workplace

Selling Time

While skilled freelancers work within somewhat predictable percentage ranges, those selling their time and physical effort face much more fuzzy pay structures.

For delivery drivers, the “percentage taken” has begun to disappear entirely, replaced by algorithms determined by factors like distance traveled, weight of deliverable items, demand, and expected time needed to make the delivery.

DoorDash, for example, pays a base rate of $2 to $10 or more, which varies based on "estimated time, distance, and desirability of the offer." Uber Eats uses a similar formula, paying for pickup, drop-off, and mileage, but changes the rates based on market demand in the moment. These factors make it difficult for drivers to know exactly how much they can expect to earn from a day’s work, and make salaries much more irregular.

Additionally, because these payouts are generally low, the system is supported heavily by client tips. Practically every major delivery service emphasizes that workers receive 100% of their tips, without the app taking any off the top. While this seems like a boon to the delivery driver, according to one report about New York delivery workers these tips end up making up the majority of their income.

In ride sharing apps, there is an even greater disconnect between what clients pay and what the drivers actually earn. Uber and Lyft generally take 25%–30% of the fare, but this can spike to 40% on short or low-fare rides.

In general, it seems that those who sell their time by offering convenience to people through delivery or ridesharing are more susceptible to tip variables, algorithms, and external circumstances.

Selling Credibility and Trust

Some of the farthest ends of the spectrum regarding gig fees are found in the care sector.

TaskRabbit, for example, is a platform where people can sell their services to individuals such as home repairs or furniture assembly and allows users to keep 100% of the rates they set. Similarly, Care.com and Sittercity, two popular babysitting platforms, take 0% of the worker’s earnings. These services act as a digital bulletin board to connect clients with people who can offer them services they need, but the platform itself does not claim responsibility for the worker. In fact, neither of these sitter platforms accept legal liability for issues that arise after the two parties have been connected, as per their terms of service.

On the other end of the spectrum, you have both Wag! charging a 40% fee, and Rover charging anywhere from 20%-25%. The difference between these two dog walking services and the sitter services is that the former actually perform extensive, third-party background checks and take on a limited amount of liability for connecting the two parties.

When it comes to care and service apps, the ones that are charging steep gig fees are the ones selling peace of mind to the clients, whereas the ones that let workers keep all their own money require them to build up their own reputations on the platform.

Infographic: LLCAttorney

Selling Wares

Finally, for those selling physical goods or renting out property, the base fees are generally low, but may include a mountain of microtransactions.

The base rate for sellers on Etsy is just 6.5% of all transactions sold on that marketplace. When compared to Fiverr's 20% base rate, this seems pretty low, however, each and every seller on Etsy gets charged a $0.20 per listing fee (regardless of whether they have any buyers) as well as a 3% payment processing fee, plus an optional 12%-15% Offsite Ads fee if the buyer was referred to Etsy through one of its marketing efforts. Similar to Etsy, eBay charges anywhere from 2.5% to 15.3% depending upon what the seller sells in addition to charging a $0.30 to $0.40 per transaction. Websites such as Amazon and Booking.com do not charge any additional fees, although their rates are typically much higher at 15% for Amazon and between 10% to 25% for Booking.com.

In the rental space, Airbnb charges a 3% Host Fee to renters who list their spaces on the platform, in addition to a 14%—16% Service Fee. Vrbo charges a 5% Commission Fee and a 3% Payment Processing Fee per listing, or a flat $700 Subscription Fee.

As we can see, physical products create an ecosystem where either the platform charges lower rates and creates many additional fees, or charges a higher rate with fewer complexities in their pay structure.

As gig work expands into multiple areas of modern economy, it is now more important than ever for workers to understand that platforms are not a monolith. The fees that these apps charge are not just the cost of using an app, but actually represent the cost of convenience, customer access and operational support. Ultimately, businesses charge different fees based upon what you are selling: your skills, your time, your credibility or your wares.

Reviewed by Ayaz Khan.

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by Guest Contributor via Digital Information World

Does ‘free’ shipping really exist? An expert shares the marketing tricks you need to know

Adrian R. Camilleri, University of Technology Sydney

You’re scrolling through an online retailer, like Amazon, Shein or eBay, and spot a shirt on sale for $40. You add it to your cart, but at checkout, a $10 shipping fee suddenly appears. Frustrated, you close the tab.

But what if that same shirt was priced at $50 with “free” shipping? The likelihood that you would have bought it without a second thought is much higher.

COVID changed the way we shop and accelerated our reliance on e-commerce. But as online sales have grown, so has the expectation of free delivery.

The reality, however, is that shipping physical goods is never actually free. Retailers use subtle marketing strategies and psychological hacks to mask these costs. As a result, consumers are often the ones footing the bill.

Image: Polina Tankilevitch / Pexels

The magic of zero

There is something uniquely attractive about the concept “free”. In behavioural economics, zero is not just a lower price; it flips a psychological switch.

When a transaction involves a cost, we instinctively weigh the downside. But when something is entirely free, we experience a positive emotion and perceive the offer as more valuable than it is mathematically.

Retailers no doubt realise that offering free delivery is one of the most effective ways to stop a consumer from abandoning a digital shopping cart.

The minimum spend trap

Perhaps the most common marketing tactic is the free shipping threshold. Sometimes this is phrased as: “Spend $55 to qualify for free shipping.”

If your shopping cart is sitting at $40, you face a dilemma. You can pay $10 for postage, or you can find a $15 item to reach the threshold. Many of us choose the latter, reasoning it is better to get a tangible product, such as a pair of socks, than to “waste” money on shipping.

This tactic uses the “goal gradient effect”, which describes the tendency to put in more effort the closer we get to a goal. It also works incredibly well for the retailer.

Research shows that free shipping increases both purchase frequency and overall order size. Policies with a threshold for free shipping often prompt this exact “topping up” behaviour. The consumer ends up buying things they did not initially want, thus boosting the retailer’s sales.

Baked-in costs and the reality of ‘free’ returns

Another strategy is unconditional free shipping, where the delivery cost is simply baked into the product’s base price. This allows consumers to avoid the “pain of paying” a separate fee at checkout. However, we are still paying for the postage through higher item costs.

For retailers, offering unconditional free shipping without a markup can be difficult to sustain profitably. The bump in sales usually does not offset the lost fee revenue and the costs of fulfilment.

A major reason for this lack of profitability is that free shipping leads to significantly higher product return rates.

Consumers tend to make riskier purchases if the appearance of waived fees lowers the perceived financial risk of the transaction.

For example, you might order the same shirt in two different sizes, knowing you can just send one back for free. Who pays for that added convenience? The retailer, who now has to cover the courier fees twice.

The retailer usually won’t simply absorb this cost, but will have to pass it on in other ways.

The subscription illusion

To combat these unpredictable costs, many businesses are turning to membership, loyalty, or subscription models such as Amazon Prime. Consumers pay an upfront annual fee in exchange for “free” expedited shipping year-round.

Membership-based programs successfully increase customer loyalty and purchase frequency, and allow for better customer segmentation.

But in the long run, they may actually hurt a retailer’s profit margins. While loyalty rises, the operational costs of fulfilling many smaller, free-shipped orders can potentially outweigh the benefits if not strictly managed.

For the consumer, this model manipulates our “mental accounting”. Because we view the upfront fee as money already spent, every additional purchase feels like it comes with a free perk. We end up shopping more frequently on that specific platform just to “get our money’s worth”.

Don’t buy the illusion

The age of limitless free shipping may be coming to an end.

As global supply chain costs remain volatile, we are likely to see retailers raising their minimum spend thresholds, removing offers, or increasing base product prices to compensate.

The next time you are shopping online, resist the urge for instant gratification.

If you are about to add a $15 pair of novelty avocado socks to your cart, just to save $10 on shipping, take a step back. Ask yourself if you truly need that purchase to arrive this week.

Instead of rushing to checkout, let your digital basket fill up naturally over time with items you actually need. You will eventually hit the threshold, but on your own terms.

“Free” delivery is just a clever psychological illusion. The cost is rarely eliminated; it is simply redistributed into higher product prices or reframed as a loyalty perk.

Don’t let the allure of “free” shipping trick you into paying for more than you intended.

Adrian R. Camilleri, Associate Professor of Marketing, University of Technology Sydney

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Reviewed by Irfan Ahmad.

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by External Contributor via Digital Information World

OpenAI Reports 900M Weekly ChatGPT Users, 50M Subscribers, 9M Paying Business Users

Reviewed by Ayaz Khan

OpenAI, on a February 27, 2026 announcement post, reported continued growth across its AI platforms, with weekly active users of ChatGPT reaching 900 million and more than 50 million consumer subscribers. Based on previous The Information (via Reuters) reporting and our calculations, the number of paying subscribers increased from roughly 35 million in July 2025 to 50 million in February 2026, an estimated increase of about 15 million users, averaging roughly 433,000 new paying users per week over the period.

Codex, the company’s software tool for building software, now has 1.6 million weekly users, more than tripling since the start of the year. Over nine million paying businesses rely on ChatGPT for business functions including engineering, support, finance, and sales.

The company highlighted partnerships with Amazon and NVIDIA to support enterprise AI development, including dedicated inference and training infrastructure. OpenAI announced $110 billion in new investment at a $730 billion pre-money valuation, including $30 billion each from SoftBank and NVIDIA and $50 billion from Amazon. The valuation also increased the OpenAI Foundation’s stake in OpenAI Group to over $180 billion.

According to OpenAI’s announcement post, these partnerships and investments aim to bring frontier AI to more people, businesses, and communities globally. 

Image: Zulfugar Karimov / Unsplash

Note: This post was improved with AI assistance and reviewed, edited, and published by humans.

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Open Letter from Google and OpenAI Employees Raises Concerns About Potential Military AI Use

Reviewed by Ayaz Khan.

An open letter, titled "We Will Not Be Divided" (as of February 28, 2026) signed by 573 current employees of Google and 93 current employees of OpenAI calls on company leadership to decline requests described in the letter as coming from the United States Department of Defense (DoD).

Screenshot: Notdivided.org / Credit: DIW

The letter claims that the department has considered invoking the Defense Production Act in connection with Anthropic and has discussed measures that could require the company to provide access to its AI models for military use. It further states that Anthropic declined to allow its models to be used for domestic mass surveillance or for fully autonomous lethal decision-making without human oversight. In line with these concerns, OpenAI CEO Sam Altman told CNBC he does not think the Pentagon should threaten AI companies with the Defense Production Act and said companies should be able to decide whether to cooperate under legal protections. On Saturday, Sam Altman also posted on X that OpenAI reached an agreement with the Department of War to deploy its models in the department’s classified network, noting that the department agrees with safety principles, including prohibitions on domestic mass surveillance and human responsibility for the use of force, including autonomous weapon systems.

Screenshot: Sam Altman - X / Credit: DIW

According to the letter, the Department of Defense has engaged in discussions with Google and OpenAI regarding potential cooperation on similar AI capabilities. The letter does not include independent verification of these claims but presents them as the understanding of its signatories.

The organizers state that all signatures were verified as current employees, and that some signatories chose to remain anonymous publicly.

Notes: This post was improved with AI assistance and reviewed, edited, and published by humans.

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