Showing posts with label healthtech. Show all posts
Showing posts with label healthtech. Show all posts

Friday, October 23, 2026

Google Fit API Shutdown Could Impact Your Fitness Devices

Rafael Henrique/SOPA Images/LightRocket via Getty Images

There was a moment not that long ago when fitness trackers seemed like a niche business. Now, they’ve become ubiquitous, both in terms of smartphones having some fitness-tracking functionality and with a growing variety of wearables on the market. But there’s a lot of code behind the scenes to make your wristband, smart ring or cellphone compile information and process it — and a recent decision from Google could shake up the industry. Earlier , Google announced that “Google Fit developer services will be transitioning to become a core part of the Android Health platform…..…Story continues…

By: Tobias Carroll

Source: InsideHook

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Critics:

Google Fit is a health-tracking platform developed by Google for the Android operating system, Wear OS, and iOS. It is a single set of APIs that blends data from multiple apps and devices. Google Fit uses sensors in a user’s activity tracker or mobile device to record physical fitness activities (such as walking, cycling, etc.), which are measured against the user’s fitness goals to provide a comprehensive view of their fitness.

Google Fit was announced at the Google I/O conference on June 25, 2014. A software development kit for Google Fit was released on August 7, 2014. Fit launched to the public on October 28, 2014. In August 2018, Google announced a revamp to its Android Fit platform which adds activity goals based on activity recommendations from the American Heart Association and the World Health Organization.

The updates are meant to help Fit better provide metrics for activities other than walking and encourage users to engage in activities that will raise the heart rate without necessarily requiring a trip to the gym. In April 2019, Google announced Google Fit for iOS offering similar experience to its Android counterpart. Google Fit for iOS used Apple Health, Nike Run Club, Headspace or connected device such as Apple Watch or Wear OS smartwatch connected to user device.

In August 2019, Google announced dark theme, sleep insights and workout map feature availability. In April 2020, Google redesigned Google Fit. In November 2020, Google Fit added iOS 14 widget. In February 2021, Google announced a Pixel 5 exclusive feature for Google Fit, heart rate and respiratory measurement using Pixel 5’s camera. In June 2021, Google announced support for paced walking. In 2022, Google began to deprecate Google Fit in favor of acquiring Fitbit and the Health Connect APIs. Google stated that the Google Fit APIs will be shut down in 2025.

Google Fit provides a single set of API for apps and device manufacturers to store and access activity data from fitness apps and sensors on Android and other devices (like wearables, heart rate monitors or connected scales). Users can choose who their fitness data is shared with as well as delete this information at any time. Wearable heart rate monitors for athletes were available in 1981.

Improvements in technology in the late 20th and early 21st century made it possible to automate the recording of fitness activities, as well as to integrate monitors into more easily worn equipment. The RS-Computer shoe was released in 1986. Early examples include wristwatch-sized bicycle computers that monitored speed, duration, distance, etc., available at least by the early 1990s.

By at least the early 2000s, wearable fitness tracking devices were available as consumer-grade electronics, including wireless heart rate monitors that could be connected to commercial-grade exercise machines in gyms. Athletes are usually tracked with the levels of internal and external loads, where external loads will consist of the performance outcomes usually witnessed by coaches, and internal loads consist of factors such as heart rate, blood pressure, and blood lactate levels.

When taking into account the well-being of the subject, subjective scales are involved which measure fatigue, sleep quality, emotions, and soreness. Activity trackers later diversified to include wristbands and armbands (so-called smart bands) and smaller devices that could be clipped wherever preferred. In 2006 Apple and Nike released the Nike+iPod, a sensor-equipped shoe that worked with an iPod Nano.

By 2010, logging apps had been introduced, many of which integrated the direct sharing of data to Facebook ot Twitter. Activity trackers became appealing to consumers because of the combination of gamification, the social dimension of sharing via social media, and increased motivation due to the resulting rivalry and competition between friends. In 2016, there were several advances made regarding fitness tracking geared toward kids with a variety of options from organizations such as UNICEF and Garmin.

Most consumer activity trackers are worn on a wristband similar to a wristwatch. This type of tracker usually includes a digital display for data. Wrist-based trackers may be prone to error during exercise involving rapid arm motion. Some activity trackers take the form of a ring. Ring-based trackers have no display of their own and rely on a connection with a smartphone to display tracked data.

Another low-profile format for activity trackers places sensors inside of earphones. These trackers rely on a smartphone to display data, similar to ring-based trackers. Earphone-based activity trackers use sensors to take readings directly from the capillaries located within the ear. Due to their placement, these trackers can give more accurate results for blood pressure, electrocardiogram, and body temperature. Activity trackers placed in the ear are also well suited to the assessment of heart rate.

Many devices primarily intended as smartwatches also function as activity trackers. An early example was the Apple Watch, which has offered has offered fitness tracker functions since 2014. The standard activity-tracking smartphone or web apps present data in statistical form meant to be viewed after the activity has ended. However, research suggests that if we want a richer understanding of the data, we need intelligent computing to be included in the systems that run the apps.

Certain movements of the user can distort the results obtained from activity trackers as seen in a test conducted by Stiftung Warentest where the products were unable to accurately track a bike ride. Furthermore, the determined values for the human energy transformation were erroneous. With the heart rate large deviations have been observed at wristlet trackers, and it is recommended for this purpose to use appropriate chest straps. Wristbands can be uncomfortable to wear and inadvertently be lost. For some products, genotoxic substances were detected.

There have been some privacy issues regarding the data collection of activity-tracking apps, a user’s health can be tracked into a “digital health footprint”. There have been many concerns about privacy issues with menstruation and reproductive health-tracking apps. Many women who use these apps for menstrual and contraceptive tracking are under the impression that their data is private when there is no single body regulating the apps, making the availability and protection of the data unknown.

The apps of some activity trackers not only transmit personal data but also private address lists to servers on the Internet without notifying or asking the user. Even when anonymized, the mere presence of geolocation data may be a national security risk. However, the results of a study among semi-professional (half-) marathon participants suggest that certain users are open to sharing tracked activity data voluntarily.

In the US in 2013, BodyMedia developed a disposable activity tracker to be worn for a week, which is aimed at medical and insurance providers and employers seeking to measure employees’ fitness. In 2014, Jawbone developed a system called UP for Groups which could provide employers with aggregated data from employees’ wearable activity trackers and apps.

Research has been carried out on the gamification of health apps, where users earn incentives and rewards based on their health goals. Though this can make the app engaging, there was concern it could trivialize health apps and deter the users from their genuine health goals. There is also research problematizing tracking devices about how we inhabit, experience, and imagine our bodies and lives.

Saturday, October 10, 2026

How AI Is Revolutionizing Health Care

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The market value of AI in the health care industry is predicted to reach $6.6 billion by 2021. Artificial intelligence is increasingly growing in popularity throughout various industries. Most of us associate AI with things like robots, Alexa and self-driving cars. But AI is a lot more than that. AI experts see it as a revolutionary technology that could benefit many industries. The impact of AI in the health care sector is genuinely life-changing. It is driving innovations in clinical operations, drug development, surgery and data management…….…Continue reading….

By: Terence Mills

Source: Forbes

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Critics:

Accurate and early diagnosis of diseases is still a challenge in healthcare. Recognising medical conditions and their symptoms is a complex problem. AI can assist clinicians with its data processing capabilities to save time and improve accuracy. Through the use of machine learning, artificial intelligence can be able to substantially aid doctors in patient diagnosis through the analyis of mass electronic health records (EHRs).

AI can help early prediction, for example, of Alzheimer’s disease and dementias, by looking through large numbers of similar cases and possible treatments. Doctors’ decision making could also be supported by AI in urgent situations, for example in the emergency department. Here AI algorithms can help prioritise more serious cases and reduce waiting time. Decision support systems augmented with AI can offer real-time suggestions and faster data interpretation to aid the decisions made by healthcare professionals.

In 2023 a study reported higher satisfaction rates with ChatGPT-generated responses compared with those from physicians for medical questions posted on Reddit’s r/AskDocs. Evaluators preferred ChatGPT’s responses to physician responses in 78.6% of 585 evaluations, noting better quality and empathy. The authors noted that these were isolated questions, not in the context of an established patient-physician relationship.

Recent developments in statistical physics, machine learning, and inference algorithms are also being explored for their potential in improving medical diagnostic approaches. Electronic health records (EHR) are crucial to the digitalization and information spread of the healthcare industry. Now that around 80% of medical practices use EHR, the next step is to use artificial intelligence to interpret the records and provide new information to physicians.

One application uses natural language processing (NLP) to make more succinct reports that limit the variation between medical terms by matching similar medical terms. For example, the term heart attack and myocardial infarction mean the same things, but physicians may use one over the over based on personal preferences. NLP algorithms consolidate these differences so that larger datasets can be analyzed.

Another use of NLP identifies phrases that are redundant due to repetition in a physician’s notes and keeps the relevant information to make it easier to read. Other applications use concept processing to analyze the information entered by the current patient’s doctor to present similar cases and help the physician remember to include all relevant details. Beyond making content edits to an EHR, there are AI algorithms that evaluate an individual patient’s record and predict a risk for a disease based on their previous information and family history.

One general algorithm is a rule-based system that makes decisions similarly to how humans use flow charts.[21] This system takes in large amounts of data and creates a set of rules that connect specific observations to concluded diagnoses. Thus, the algorithm can take in a new patient’s data and try to predict the likeliness that they will have a certain condition or disease. Since the algorithms can evaluate a patient’s information based on collective data, they can find any outstanding issues to bring to a physician’s attention and save time.

One study conducted by the Centerstone research institute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response. These methods are helpful due to the fact that the amount of online health records doubles every five years. Physicians do not have the bandwidth to process all this data manually, and AI can leverage this data to assist physicians in treating their patients.

Improvements in natural language processing led to the development of algorithms to identify drug-drug interactions in medical literature. Drug-drug interactions pose a threat to those taking multiple medications simultaneously, and the danger increases with the number of medications being taken. To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature.

Efforts were consolidated in 2013 in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms. Competitors were tested on their ability to accurately determine, from the text, which drugs were shown to interact and what the characteristics of their interactions were. Researchers continue to use this corpus to standardize the measurement of the effectiveness of their algorithms.

Other algorithms identify drug-drug interactions from patterns in user-generated content, especially electronic health records and/or adverse event reports. Organizations such as the FDA Adverse Event Reporting System (FAERS) and the World Health Organization’s VigiBase allow doctors to submit reports of possible negative reactions to medications. Deep learning algorithms have been developed to parse these reports and detect patterns that imply drug-drug interactions.

The increase of telemedicine, the treatment of patients remotely, has shown the rise of possible AI applications. AI can assist in caring for patients remotely by monitoring their information through sensors. A wearable device may allow for constant monitoring of a patient and the ability to notice changes that may be less distinguishable by humans. The information can be compared to other data that has already been collected using artificial intelligence algorithms that alert physicians if there are any issues to be aware of.

Another application of artificial intelligence is chat-bot therapy. Some researchers charge that the reliance on chatbots for mental healthcare does not offer the reciprocity and accountability of care that should exist in the relationship between the consumer of mental healthcare and the care provider (be it a chat-bot or psychologist), though. Since the average age has risen due to a longer life expectancy, artificial intelligence could be useful in helping take care of older populations.

Tools such as environment and personal sensors can identify a person’s regular activities and alert a caretaker if a behavior or a measured vital is abnormal. Although the technology is useful, there are also discussions about limitations of monitoring in order to respect a person’s privacy since there are technologies that are designed to map out home layouts and detect human interactions.

Artificial intelligence algorithms have shown promising results in accurately diagnosing and risk stratifying patients with concern for coronary artery disease, showing potential as an initial triage tool. Other algorithms have been used in predicting patient mortality, medication effects, and adverse events following treatment for acute coronary syndrome.

Wearables, smartphones, and internet-based technologies have also shown the ability to monitor patients’ cardiac data points, expanding the amount of data and the various settings AI models can use and potentially enabling earlier detection of cardiac events occurring outside of the hospital. Another growing area of research is the utility of AI in classifying heart sounds and diagnosing valvular disease.

Challenges of AI in cardiovascular medicine have included the limited data available to train machine learning models, such as limited data on social determinants of health as they pertain to cardiovascular disease. A key limitation in early studies evaluating AI were omissions of data comparing algorithmic performance to humans.

Examples of studies which assess AI performance relative to physicians includes how AI is noninferior to humans in interpretation of cardiac echocardiograms and that AI can diagnose heart attack better than human physicians in the emergency setting, reducing both low-value testing and missed diagnoses. In cardiovascular tissue engineering and organoid studies, AI is increasingly used to analyze microscopy images, and integrate electrophysiological read outs.

Healthcare Systems Turn To AI To Help Ease Strain On Hospitals In Massachusetts Bisnow 21:22 Thu, 23 May 

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