Nolan Parker’s Post

Excited to share that I've officially joined PushPress as a Senior Product Manager! 🎉 With a background as both a coach and former affiliate owner, I’ve seen firsthand the unique challenges gym owners and coaches face daily. That’s why I’m so excited to be part of a company that’s redefining gym management software and empowering gym owners and coaches to thrive. Big thanks to the team at PushPress for the warm welcome. I can’t wait to dive in and help coaches and gym owners reach new heights! 💪

After reading this Harvard Business School paper “AI Companions Reduce Loneliness” (https://lnkd.in/gHHHzhZ3) I spent some time mulling the opportunity and the potential hurdles to overcome as we deploy these solutions… Wins 🏆 ▪Accessibility and Convenience: #AI-driven platforms can offer mental health support anytime and anywhere, breaking down barriers to access for those who may not have the means or ability to attend in-person sessions.  ▪#Personalized Care: AI can analyze vast amounts of data to tailor recommendations and interventions to the individual. This level of personalization can enhance the effectiveness of treatment plans. AI journaling companions, for example, can provide real-time feedback and support based on the user's inputs, adapting to their needs and progress. ▪Data-Driven Insights: AI can help identify patterns and predict mental health issues before they become severe. By analyzing behavioral data, AI can alert healthcare providers to potential concerns, enabling early intervention and more proactive care. ▪ Reduction of #Stigma: For some, interacting with an AI can feel less intimidating than speaking with a human therapist, potentially reducing the stigma associated with seeking help for mental health issues. This can encourage more individuals to seek support. Considerations 🤔 ▪Lack of Human Empathy: One of the biggest challenges lies in creating AI systems that complement rather than substitute the inherently human aspects of communication and empathy. The subtle nuances of human emotion and the warmth of human interaction are difficult to replicate with AI, which can lead to feelings of isolation. This may improve with time. ▪Paradox of Connectivity: While AI is often designed to enhance connectivity, it can paradoxically contribute to a sense of isolation. This highlights the need for balance between AI and human interaction to avoid exacerbating loneliness. ▪Dependence on Technology: Over-reliance on AI for #mentalhealth support can lead to decreased human-to-human interaction, which is vital for emotional well-being. The risk is that AI becomes a crutch rather than a supplement, potentially undermining the development of healthy interpersonal relationships. ▪Privacy and Ethical Concerns: The use of AI in mental health care raises significant privacy and ethical issues. The handling of sensitive personal data must be managed with the utmost care to protect user confidentiality and trust. This leads me down the path of what it means to be human and how we evolve alongside AI companions but I don’t have well-defined thoughts on that topic yet. 🙂

The personal health renaissance has already begun... I’ve always considered myself a realist, generally an optimist, and occasionally a romantic. I don't spend too much time worrying about the future or attempting to predict it. There are so many evolving variables and unknowns that it never felt like a good use of time. But there's one area I can't help but take a glimpse into the future. The direction that health (wellness, fitness, nutrition, healthcare) is heading feels inevitable to me. A human-driven experience with processes and insights optimized by AI/ML. This will apply to how we solve problems related to our health at every stage of life. The speed at which we get there will depend on many factors. Themes that continue to emerge: Optimism for the Future: -There's an inevitable shift toward a more connected and integrated health landscape with a focus on patient access and user centricity. -The transformative power of combining various data sources in an IoT fashion leveraging wearables, home health hubs, phones, voice, and facial recognition, and even spending patterns to solve problems, identify issues, and enhance our lives and well-being. AI will help this seamlessly blend into the background. 👉 If the massive amount of user information out there can be harnessed with integrity and intention, it will change the world. The Role of AI and Personalization: -The industry is moving towards user-centric and patient-centric experiences, utilizing data-driven recommendations. The integration of AI and machine learning is personalizing both health and wellness experiences. -AI's potential to automate and scale operations is being applied to the optimization of processes as much as the insights and predictions side. 👉 "The promise of AI is no UI." - Naval Ravikant Customer Acquisition and Market Dynamics: -Effective customer acquisition channels blend digital marketing, strategic partnerships, and influencer marketing. The way people seek out information to solve their problems has changed dramatically. Experts and authorities need to meet people where they're at. -Market saturation is real, and making unique value propositions is essential in this evolving landscape. Many solutions are focusing on solving a broader set of problems for niche audiences. Any app, product, or service trying to "do it all" and "build it all" is wasting time and resources. Collaboration: -The fragmentation of data, capital, and talent is a limiting factor, especially when it comes to innovation. There's a higher cost to play. Look around to see who you can work together with. Don’t try to solve every problem yourself when it comes to deploying your solution. At the end of the day I have to ask...can we get out of our own way (systems, bureaucracy, flow of dollars, regulation, greed) to make this happen? I'll continue to stare dramatically over my shoulder until I know for sure...

One of the biggest mistakes I see businesses make with wearable data is treating each day's data in isolation. When it comes to designing an experience, trend analysis is paramount. Trends over time provide far more valuable insights than single-day snapshots. Why? Because we are complex systems, and our lifestyles tend to average out over time. Any given day could be an anomaly. Some of the most common use cases... Sleep Patterns: By consistently analyzing sleep patterns over weeks or months, we can highlight underlying issues that might not be apparent from just one night's data. For instance, identifying poor sleep hygiene trends is far more useful than focusing on isolated poor sleep nights. Physical Activity/Cardiac Load: Tracking physical activity trends allows us to understand user behavior better and identify long-term improvements or declines. For example, recognizing patterns of increased activity can lead to improved cardiovascular health over time. Ramping up too quickly could create too much stress and lead to burnout. Recovery Metrics: Monitoring recovery trends is crucial for providing better recommendations for rest and recovery periods. Observing these trends helps prevent overtraining and ensures balanced workout routines. A baseline needs to be established to understand deviation and trends. The bottom line: Recommendations based on single days' data can be misleading and frustrating for users. We need to consider how data points are connected over time, as well as how one day's data can affect the next, to avoid errors in interpretation. Recognizing that the effects of one day's activity can influence the next can improve the quality of recommendations, ensuring they are based on cumulative rather than isolated data.

The integration of new technology and data has led to significant changes in the way #insurance companies assess risk. I just had an amazing chat with Isaac Espinoza about this. He shared highlights about how Root Inc. uses #telematics to make better decisions and improve its product offering for auto insurance. It sounded exactly like the way wearable data is being used by life insurance companies to understand users' health. As a User -Judge me based on my behavior and actions. ✅ -Don't lump me in with the "average" driver or "typical" health profile. ❌ -If I drive with caution and prioritize my health I want to be recognized and rewarded for that! 🏆 Telematics in Auto Insurance 🚗 Telematics involves using location data to monitor and assess driving behavior. This data is used to price and score policies based on actual driving habits rather than demographic proxies. Drivers who are willing to share their driving data can prove they are safer, potentially earning lower premiums. Use Case ✍ -Data Sharing for Safety: Policyholders voluntarily share their driving data to demonstrate safe driving habits. -Behavior-Based Scoring: Real-time data leads to more accurate risk assessments and personalized pricing. -Gamification: Programs often include elements of gamification, encouraging better driving through rewards and feedback. Wearables in Life Insurance ⌚ #Wearables, such as fitness trackers and smartwatches, offer a parallel in the life and health insurance sector. These devices monitor health metrics like heart rate, activity levels, and sleep patterns, providing insurers with detailed insights into policyholders' lifestyles. Use Case ✍ -Transparency: Users share their #healthdata, demonstrating their commitment to a healthier lifestyle. -Behavior Monitoring: Continuous data collection allows for dynamic risk assessment and personalized health advice. -Gamification and Engagement: Many programs include challenges and rewards to motivate users to maintain or improve their health. Other Considerations 🤔 -Behavioral Proxies vs. Direct Data: Using direct behavioral data is more predictive and fair compared to traditional proxies like credit scores in auto insurance. -Real-Time Feedback: Both sectors benefit from real-time feedback to improve user behavior and prevent claims. -Integration of Data: The integration of telematics or wearable data with insurance company databases enhances the accuracy of risk models and the effectiveness of prevention strategies. The parallels between telematics in auto insurance and wearables in life insurance highlight a significant shift towards data-driven, behavior-based risk assessment. By leveraging real-time data and promoting healthier behaviors, insurers can offer more personalized and fair pricing, ultimately benefiting both the insurer and the insured. This approach not only enhances risk management but also fosters a proactive stance towards health. ROOK Marco Benitez Jonas Dücker

About a month ago, I got bitten by the endurance bug. 🐞 It was Saint Francis Tulsa Tough weekend, I provided on-course support for friends doing the 100-mile Ace peloton ride. It was so fun I started searching for my own races while driving between rest stops. Bikes are expensive so I figured a long run would be a great start. I stumbled upon the Catamount Ultra Marathon 25km happening just four weeks later in my beautiful home state of Vermont. I hadn't competed in anything since January so I was due. Why not just do another CrossFit, LLC competition? In CrossFit, I sometimes focused too much on sport-specific training and neglected GPP. My body didn’t always recover well, sometimes I would focus on skill work rather than work capacity. Typically, heavier and more technically challenging competitions suited me better but I didn't always feel my health improving. I missed the simplicity of training for a monostructural, one-modality event. Training for an endurance race like the Catamount Ultra differs significantly from CrossFit. Gommaar D'Hulst at WOD Science offers great insights into how endurance athletes' training and capacity differ from CrossFit athletes. The adaptations are primarily related to VO2 max capacity. The sheer volume of time spent on improving aerobic capacity makes a big difference. An elite cyclist or runner may have a VO2 max of 70-85 ml/kg/min, while the top CrossFit athletes sit in the 55-70 ml/kg/min range. Different sports require different demands, and that’s the tradeoff for high-level gymnastics skills and Olympic lifting numbers. Back to the story! The race couldn’t have gone better. After aggressive climbing the first 4.5 miles I was able to make up time running hard on the downhills. At certain points holding a 5:30 mi/pace. Polar Electro Oy and WHOOP certainly knew I was at my limits with a Very High Cardio Load and Strain score of 20.6. Crossing the finish line at 2:45:41, I honestly felt phenomenal. The announcer joked that he would have to arm wrestle me and I immediately got in line for a maple creemee, Trapp Family Lodge beer, and a burrito. 🍦 🍺 🌯 I fueled with simple and complex carbs and kept my water intake consistent with electrolytes. Maurten UCAN UnTapped Maple Drink LMNT I only had 4 weeks to train and knew that turnaround time was not enough to improve my fitness for the race. I focused more on getting comfortable running with more frequency at lower heart rate zones, stretching for injury prevention, and race day execution on my fuel and pacing. I built up to my last long run six days before the race—12 miles with some elevation on a hot, dusty 95-degree day in Tulsa. Vermont's 61-degree weather and light rain felt much better, despite the additional elevation gain and distance. Any favorite trail runs I can tackle next? ROOK Marco Benitez Jonas Dücker

I’m pretty jazzed about how AI and wearable data are teaming up to change the game in personal health and wellness. Using wearables to count steps or monitor heart rate is a thing of the past. This data now gets a second life to feed models that personalize health coaching and give you real-time advice and insights. Since ChatGPT made custom bots available I started training my own Nonobot🤖 to help answer my own health and wellness questions. It’s cool and fun to play with, but I know there is so much more opportunity with models that are trained to perform these functions at scale. I'm even more impressed with what I see some ROOK clients doing with the data to generate insights as well as predictive models. Google recently released an analysis of how their Gemini Ultra 1.0 health LLM performed against various benchmarks (link in comments). The article does a great job of capturing how a health LLM is trained and what to consider in benchmarking performance. A swath of companies are exploding on the scene with personal concierge health models powered by AI and including data from your favorite wearables as well as blood work, genetic data, CGMs, and more. For this movement to succeed, companies will need to capture more than just the early adopters. Biohackers and folks paying top $ for concierge medicine won't be enough. New ventures in this space will need some serious special sauce to break out. Why is that? The big players in the industry are already on the march. With Google's launch of Health Connect and acquisition of Fitbit they are poised to capture the data and feed their Gemini model and deliver these insights. Meanwhile, Apple has made it clear that they are going deeper down the health and AI tracks. With these market leaders getting a firm footing in the space it will be tough to oust them. They have deep penetration with their own wearables and aim to wrap up data from other sources through their health kits. So what can we expect to see… AI helps handle the large volume of data from wearables. Models can be trained to analyze information, spot trends, and provide advice based on your unique health profile. For example, if you're having trouble sleeping, the AI can look at your sleep patterns, find issues, and suggest ways to improve your rest. The same goes for fitness—tracking your progress, offering tips, and helping you get the most out of your workouts. Eventually this line will blur with the health landscape, but regulation will slow this down. So will the perfect prescription from an AI Coach result in the next health renaissance? 👨🎨 I wish! But it won’t be that simple. There's also the other side of the coin. Behavior change is difficult for many people. Paired product/service solutions that combine AI insights with user-centric coaching will be the knockout punch to truly move the needle. #HealthTech #AI #WearableTech #PersonalHealth #Innovation #Wellness Marco Benitez Jonas Dücker

People named Alan/Allen, who go by "Al", please start using your full name or I will call you #ai by accident... 🤖 🤦♂️ ❌

Come say hello at InsurTech Ohio 👋 Tony Cañas, CPCU, MBA, AU, ARM, ARe, AIC, AIS ROOK Marco Benitez Jonas Dücker #insurance #wearables