LifeJig

The burden of Non-Communicable Diseases (NCDs) such as diabetes and cardiovascular diseases is mounting in LMICs. Two thirds of obese people and 80% of people living with diabetes in the world live in LMICs while 80% of mortality from cardiovascular diseases is in LMICs. (1-3)

In LMICs with populous and more urbanized demographics, the percentage of disease burden from NCDs is approaching that of upper income countries. But in these countries, NCDs are generally affecting younger people - midlife - as compared to the elderly in advanced countries. Consequently, the projected number of disability-adjusted life-years (DALYs a.k.a. “healthy years”) lost due to NCDs will be the highest in LMICs compared to LICs – which are generally less populous and have more rural demographics - or advanced countries because the latter are better prepared. (4) Therefore, the relative impact of NCDs on premature mortality is the highest in LMICs and experts recommend significantly more healthcare investments in LMICs to prevent and treat NCDs. The figure below shows that soon, the number of millions of life-years lost due to NCDs is about five folds higher in LMICs than in LICs.

Source: doi: 10.1377/hlthaff.2017.0708

Majority of chronic NCDs are preventable with therapeutic lifestyle change or are manageable with effective medical interventions, but there are currently no consistent measures that assess primary care’s performance to reverse current trends in the global rise of NCDs.

In order to reach the UN’s Sustainable Development Goal of reducing premature deaths from NDCs by a third over the next 15 years, healthcare systems in LMICs need to transition expeditiously from episodic care focused on communicable diseases to relational, longitudinal and integrated care better adapted for chronic diseases. (5)

Digital Health offers several promising, affordable solutions to increase awareness, access and treatment capacity in LMICs. Over 500 digital health or mHealth initiatives have been launched in LMICs with the following objectives:

• Raising Public Awareness
• Patient Monitoring & Compliance
• Health Information Technology & Provider Support
• Remote Diagnosis & Treatment Access

Most of these have been 1-way or 2-way SMS based mHealth interventions. But depending on the digital readiness of the country, app-based and telehealth interventions have also been successfully implemented. LMICs in Africa, South and Southeast Asia and in particular, Rwanda, Ethiopia, Malaysia, India have taken the lead in standing national digital health strategies and establishing successful private-public sector partnerships. In other LMICs, it is the private sector – in particular, the information and communication multinational companies (ICTs) that have established growing digital health subsidiaries: Orange Health (Africa), Telefonica Health (Latin America), Vodacom Healthcare (Africa), Vodafone Foundation (Africa) and Philips and Telkom Indonesia are examples of such B2B and B2G ventures. (6-10)

Recent studies of mHealth implementation in LMICs show that cell phone use and internet access are no longer the major barriers. Local governance and policies, population awareness and attitudes as well as funding are among the most prevalent barriers in LMICs. (11,12) We believe that our solution does not require an advanced digital infrastructure or technology savviness; requires minimal education or training for implementation; is readily adaptable to local languages and cultures; is affordable and scalable.

LifeJig's person-centric design offers a value proposition to millions of people living in LMICs who are in need of effective lifestyle change solutions aimed at NCDs. Because LifeJig is a valued resource, users enter data as they willingly participate in the self-coaching digital programs. Thus, LifeJig solves the problem of collecting reliable baseline and longitudinal data for assessing performance of Primary Care. LifeJig is a scalable, yet personalized mHealth solution that goes beyond fragmented disease care and serves as a Core Indicator for primary care Outcomes per PHCPI.*

(* https://improvingphc.org/phcpi-core-indicators)

References:

1. Obesity in Low- and Middle-Income Countries: Burden, Drivers, and Emerging Challenges. Ford et al. Annu. Rev. Public Health 2017. 38:145–64

2. Diabetes comorbidities in low and middle-income countries: An umbrella review. Lam et al. J Glob Health 2021;11:04040

3. Cardiovascular, respiratory, and related disorders: key messages from Disease Control Priorities, 3rd edition. Prabhakaran et al. Lancet. 2018 March 24; 391(10126): 1224–1236

4. Lower-Income Countries That Face The Most Rapid Shift In Noncommunicable Disease Burden Are Also The Least Prepared. Bollyky et al. HEALTH AFFAIRS 36, NO. 11 (2017): 1866–1875

5. NCD Countdown 2030: pathways to achieving Sustainable Development Goal target 3.4. Lancet 2020, 396:918-934.

6. Unlocking digital healthcare in lower- and middle-income countries. © 2021 McKinsey & Company. 

7. https://www.gsma.com/mobilefor...

8. mHealth Application Areas and Technology Combinations. A Comparison of Literature from High and Low/Middle Income Countries. Haitham Abaza; Michael Marschollek. Methods Inf Med 2017; 56(Open): e105 – e122

9. Digital Health and Inequalities in Access to Health Services in Bangladesh: Mixed Methods Study. Ahmed et al. JMIR Mhealth Uhealth 2020 | vol. 8 | iss. 7 | e16473 |

10. Review Bridging the Human Resource Gap in Primary Health Care Delivery Systems of Developing Countries With mHealth: Narrative Literature Review. Goel et al. JMIR Mhealth Uhealth 2013 | vol. 1 | iss. 2 | e25 |

11. Barriers to the Use of Mobile Health in Improving Health Outcomes in Developing Countries: Systematic Review. Kruse et al. J Med Internet Res 2019;21(10):e13263

12. Mobile Health (mHealth) Approaches and Lessons for Increased Performance and Retention of Community Health Workers in Low- and Middle-Income Countries: A Review. Källander et al. J Med Internet Res 2013 | vol. 15 | iss. 1 | e17 |  

LifeJig® is an automated coaching program aimed at NCDs and provides a measure of primary care performance in LMICs. LifeJig has several appealing features for primary care in LMICs:

• LifeJig empowers users to prevent and/or reduce disease burden by engaging them in behavior modification aimed at physical and mental wellness;
•  It collects personal data in a low-cost, shareable and accurate way using a software application that has been clinically tested in office and online;
• LifeJig uses machine learning to provide Primary Care Practitioners (PCPs) user-specific predictors for success in reaching targeted goals;
• Healthcare quality is often measured by examining outcomes retroactively. LifeJig measures the quality of primary care "in real time" to the extent that it directly assesses success or failure of patient "enrollees."
• LifeJig explores factors that influence primary care as they pertain to interactions of persons, practitioners and their communities. As such it is a unique indicator for quality of healthcare services in each country.

The profiling data is collected using daily questions that are sent to users over a period of approximately two weeks. Users are then enrolled in one of several clinical tracks selected by their PCP. The PCP enrolls each patient in a track and scores their initial clinical status but is not burdened with longitudinal follow-up data collection.

1- “Pocket Coach” for Lifestyle Modifiable Chronic Diseases

LifeJig uses a novel concept: Lifestyle Modifiable MultiMorbidity Clusters (LMMCs). These are common chronic NCDs such as obesity, diabetes, cardiovascular diseases that are clinically associated in that preventing or managing one condition impacts other linked conditions. LifeJig uses LMMCs to assign individuals to personalized tracks; it measures their whole health status at enrollment and follows their progress for one year - renewable.

Based on our clinical experience we have selected the following LMMCs:

• Obesity
• Metabolic Syndrome & Diabetes
• Cardiovascular Diseases
• Chronic Respiratory Disorders & Allergies
• Musculoskeletal & Chronic Pain Disorders
• Addiction & Mental Health Disorders
• Hormone Dependent Cancers

Each cluster has a series of linked conditions organized based on the natural history of disease progression: If a patient develops additional morbidities, their score increases (bad outcome) and if they reverse conditions or are compliant with treatment, their score decreases (desired outcome.) For example, the figure below shows the scoring algorithm and the progression of Metabolic Syndrome also called “pre-diabetes” toward its linked conditions and complications (each stage is scored 0 – no, or 1 - yes):

Maximum Score (worst): 7; Minimum Score (best): 1

2- Primary Care Quality Score Keeper

Healthcare quality standards should be universal. In USA, the National Academy of Medicine states that a measure of quality of Primary Care should assess the healthcare system and the healthcare consumer’s status, needs and aspirations. (1)

In addition to measuring access, affordability and efficacy, the healthcare system should be accountable for improving equity and inclusiveness for all demographics. The measure should also assess a patient’s health status in a person-centered fashion that goes beyond managing individual diseases and promotes health enhancement by means of prevention, self-assessment and self-learning in a framework that is cognizant of social determinants of health.

LifeJig provides an initial score of a patient’s profile that includes, lifestyle, behavioral, medical and psychological metrics. More specifically, diet, activity, sleep quality, stress level, unhealthy behaviors, mental state, happiness score, risks and use of preventive screenings, medical conditions and treatment compliance are all scored.

The PCP scores the patient for 25 chronic conditions associated with major NCDs: 0 if they DO NOT have the condition; 1 if they have the condition; -1 if they have the condition and they are appropriately treated, or the condition is under control. Then, the PCP assigns the patient to a LMMC pathway in our digital therapeutic app, LifeJig.

The total score combines PCP’s assessment and patient’s profile. A patient’s score can be improved overtime. For example, a patient with a score of 5 in the diabetes track who lowers their blood pressure and cholesterol (with medication or lifestyle change or both), will have their score lowered from 5 to 3 (improved.) Other self-reported changes may improve the score as well: If a user reports weight loss, regular exercise or improved sleep hygiene, etc., their score improves. Patients are rescored every 3 months. In essence, the quality score keepers are “whole” patients themselves.

Some goals are specific to a track (for example % weight loss for obesity or % drop in HbA1c for diabetes) and some are common to all tracks such as engagement, self-regulation, compliance, resilience and success in reaching targets.

During this programmatic and automated coaching process, LifeJig tracks improvement in users’ scores. Patients’ aggregate score reflects PCPs' performance. As users improve their score, they and their PCPs are rewarded with incentives.

3- Person-Centric Automated Coaching using Machine Learning supported by Artificial Intelligence

After profiling, users are enrolled in self-coaching tracks. They receive daily motivational tips and quizzes that are sent via SMS and/or email. While daily tips serve as “nudges,” quizzes are designed to challenge users to make decisions in hypothetical situations. These hypothetical scenarios (extracted from real-life experiences of our patients over the last 8 years) act like a “drill” or a “simulation” and force users to think about what alternative choices they could make. This method of preparing users’ mindsets for possible surprises or setbacks - also referred to as counterfactual thinking - enhances commitment and resilience for behavior change.

Quizzes also test users’ “life skill” deficits. We have identified five skills critical for therapeutic lifestyle change. (Life skills and related quizzes are described later.) By answering a variety of quizzes, a pattern may emerge that identifies a particular life skill deficit. This approach helps assemble a behavioral profile for each user who can then receive more personalized coaching tips.

AI-driven health bots designed to respond to nutrition, exercise and life coaching inquiries are used to increase user engagement. These specialized health bots are trained on didactic materials that we have written to educate users. Bots are also trained on our quizzes. So, a user can ask bots questions related to quizzes or ask general questions related to lifestyle change or their conditions. Health bots increase engagement, self-assessment and self-learning.

LifeJig generates reports every three months and sends them to the PCP. In addition to the score, these reports indicate engagement, biometric changes and other parameters including condition-specific targets. Periodically, a report may be generated that aggregates a PCP’s patient pool of scores and serves as a quality control tool.

References:

1- Primary Care Measures and Use: Powerful, Simple, Accountable in Implementing High-Quality Primary Care: Rebuilding the Foundation of Health Care. The National Academies Press, 2021

LifeJig serves people living in LMICs who are at risk of disease and/or who need medical care for NCDs. Our solution impacts their lives directly by reducing their disease burden and enhancing their quality of life.

As demographics in LMICs are getting older and adopting more urbanized lifestyles, the prevalence of chronic multimorbidities is rapidly rising. Multimorbidities are also impacted by rising obesity and diabetes – which are linked to many associated comorbidities - physical inactivity, harmful behaviors such as alcohol and tobacco addiction as well as societal factors namely, financial stress, life setbacks, political conflicts and poor health literacy. Living with multimorbidities is linked to lower quality of life, increased premature mortality and healthcare costs. (1-3)

LifeJig is a person-centered digital coaching tool for patients. Because it is helpful for self-assessment and self-learning, it raises awareness and educates users. LifeJig’s coaching simulations enhance self-regulation, commitment and resilience for adopting new healthy behaviors. It is a valued resource for patients and their communities. 

LifeJig collects personal data in a low-cost, shareable and accurate way without any burden on PCPs. It measures health data "before and after" enrollment in lifestyle change self-coaching tracks. Therefore, it serves as a quality measure and gives feedback to practitioners as it engages patients in their own medical care.

We believe that LifeJig establishes a value proposition for mid-life individuals facing multimorbidities who will benefit directly from its deliverables: a PCP-connected, trusted and clinically tested engagement platform for behavior modification as well as an educational resource for common multimorbidities. Finally, clinical practitioners will benefit from LifeJig as a resource for outreach and management as well as a real-time quality control tool.

References:

1. Multimorbidity of non-communicable diseases in low-income and middle-income countries: a systematic review and meta-analysis. Asogwa et al. BMJ Open 2022;12: e049133. doi:10.1136/ bmjopen-2021-049133

 2. Multimorbidity of chronic non-communicable diseases in low- and middle-income countries: A scoping review. Abebe et al. Journal of Comorbidity. 2020; Volume 10: 1–13.

 3. Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries: a systematic review. Allen et al. Lancet Glob Health. 2017 Mar; 5(3): e277–e289.

Reza Yavari, MD

As an endocrinologist, the team leader, Reza Yavari MD, has been delivering therapeutic lifestyle change for diabetes and obesity for the last 20 years. His programs have been offered to patients and employees in-office, work-site, and online nationwide in the US. Recently, the company launched a digital therapeutic, Beyond Weight, which uses Simulation Coaching™ as a method for automated lifestyle change and disease risk reduction focused on obesity and diabetes. We believe that we can easily extend our digital coaching approach to other lifestyle-modifiable chronic morbidities and use it as a measure for primary care performance for NCDs.

Dr. Yavari is a US citizen, but he was raised in Iran and France. He is fluent in Farsi and French. As a physician, he has taken care of patients of diverse ethnicity in inner-cities and suburban settings in France, CT and CA. Taking care of Haitian, Jamaican and Puerto Rican patients in Bridgeport, CT, Dr. Yavari became cognizant of cultural differences that influence behavior pattern and lifestyle choices. First, the Beyond Weight App was translated in its entirety to Spanish by a teacher living in Argentina and was reviewed by a Spanish speaking nutritionist. Latin and Hispanic style recipes were added to the app. Before the pandemic, the company was in early discussions to launch the Beyond Weight App in Puerto Rico. These discussions are resumed but require additional funding. We expect that LifeJig will require modifications when it is launched in diverse countries. However, we believe that our core approach is adaptive to most LMICs.

Martha Tamez, DSc

Postdoctoral Fellow in the Nutrition Department at Harvard T.H. Chan School of Public Health

Dr. Tamez is a US permanent resident and Nutritional Epidemiology expert. Her primary field of research is in diet-related disparities that contribute to the burden of chronic diseases such as cardiovascular disease in minority populations. Her academic training and research experience span a broad background in Nutrition. Dr. Tamez received a B.A. in Nutrition and Wellbeing from the Monterrey Institute of Technology in Mexico and conducted her master's studies at the National Institute of Public Health in Mexico (INSP) elucidating relationship between dietary and lifestyle factors and chronic diseases such as obesity, diabetes, and cardiovascular disease using data analyses from large observational prospective epidemiologic studies. Dr Tamez completed her doctoral training at Harvard T.H. Chan School of Public Health, and continued studying the relationship between diet, lifestyle behaviors, and chronic diseases among Hispanics/Latinos. Dr. Tamez doctoral dissertation consisted of creating a dietary score (a traditional Mexican diet score) and its association with the risk of hypertension among U.S. adults of Mexican heritage from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Dr Tamez is currently part of the Puerto Rico Assessment of Diet, Lifestyle, and Diseases (PRADLAD), where she is responsible for data management. Through her collaborations, Dr. Tamez is attempting to further the use of digital tools, data analytics, and artificial intelligence for nutrition management for the betterment of these types of communities both in the US and Latin America.

Luis R. Soenksen, PhD

Co-founder Beyond Weight

Dr. Soenksen is a US permanent resident, serial entrepreneur, bioengineering expert, and co-founder of Beyond Weight, where he is bringing machine learning and artificial intelligence tools to the field of nutrition. Luis has held various research and entrepreneurship positions at MIT and the Wyss Institute for Bioinspired Engineering at Harvard University, where he led the development and launch of multiple cutting-edge biotechnologies and AI ventures with faculty and students across MIT and the broader Boston biotech ecosystem. Luis holds a Ph.D. in Mechanical Engineering from MIT, as well as a Master of Science in Bioengineering from Johns Hopkins University and a bachelor’s degree in biomedical engineering from the Monterrey Institute of Technology in Mexico. Luis's work has been published in several high-impact journals such as Science and Nature, research that has also led to several patented biomedical technologies and co-founding of 4 start-up companies internationally. In collaboration with Dr. Yavari and Dr. Tamez, Dr. Soenksen is supporting the development of digital tools, data analytics, and artificial intelligence systems for nutrition management and public health across a variety of communities in the US, Spain, and Latin America. 

Michael Brines, MD, PhD

As a scientist, entrepreneur and formerly Professor at the Yale School of Medicine, Dr. Brines has extensive experience in data science and is a co-inventor of Yi, the Yavari Algorithm that measures body composition (using just a measuring tape and a scale) with an accuracy close to a DXA scan and predicts risk of diabetes (type 2) comparable to HbA1c. Dr. Brines has extensive experience with the FDA and will be instrumental in our plan to obtain FDA approval for LifeJig.

Bruce Lipian, MBA

Bruce is a founding principal of a private equity firm, Stone Creek Capital. At Beyond Care, also as a principal, he oversees financial matters and reviews equity and contractual negotiations.

At Beyond Care, we have been practicing preventive medicine built on a coaching platform for lifestyle change and behavior modification for the last two decades. We have seen how effective it has been in preventing or reversing multiple morbidities. But we have also become aware of its limitations: implementation and dissemination of such structured programs are constrained by many roadblocks including access and cost. We have been working hard to overcome such barriers by launching a digital automated and affordable coaching approach that makes it possible to deliver such services in under resourced communities. As explained earlier, most of the premature mortality in LMICs is due to lifestyle modifiable multimorbidities related to obesity, diabetes, cardiovascular and respiratory diseases. Our solution can help implement and disseminate affordable primary care preventive services for these NCDs. But we know that we need help to stand our solution in LMICs.

We are also aware that our proposal faces many additional extrinsic barriers in LMICs. These include, poor funding, government regulations, low public awareness of mHealth and health literacy as well as cultural norms and beliefs. We are excited to apply to this Challenge because we are hopeful that the Bill & Melinda Gates Foundation and the MIT Solve network will help us overcome these obstacles. Tapping into the international network of connections the Foundation and Solve offer, we are looking forward to potential collaborations and partnerships with local and regional organizations and companies. We are also looking forward to business and technological support we may receive from the Foundation and Solve. We are looking for social impact investors, software engineering know-how and a network of international partners including local and regional healthcare organizations, companies such as ICTs mentioned earlier, and NGOs involved in health promotion and advocacy.

We believe our solution is uniquely fit for the rising prevalence of chronic NCDs - a challenge that will impact primary care in LMICs for decades. The following features make our solution innovative:

1 - Deep, Proprietary and Clinically Tested Phenotyping of Users:

• Body composition analysis using non-invasive biometrics called Yi - Yavari Indicator *
• Lifestyle patterns i.e. eating habits, exercise history & activity level, sleep hygiene and unhealthy behaviors
• Psychographics, including stress profile, depression & anxiety, personality traits, and perceived degree of happiness
• Medical phenotyping including disease risks, screenings, diagnoses, access to and use of primary care services

* With just a measuring tape and a scale Yi accurately estimates body fat%, abdominal fat, and muscle mass, diabetes and metabolic syndrome risk and sets weight loss target.

PLoSOne:https://doi-org.ezproxyberklee.flo.org/10.1371/journal.pone.0024017 

2 - Coaching Tracks designed for common Lifestyle Modifiable MultiMorbidity Clusters (LMMCs): Based on our clinical experience we have selected the following LMMCs:

• Obesity
• Metabolic Syndrome & Diabetes
• Cardiovascular Diseases
• Chronic Respiratory Disorders & Allergies
• Musculoskeletal & Chronic Pain Disorders
• Addiction & Mental Health Disorders
• Hormone Dependent Cancers

Because these LMMCs all require similar coaching methods for lifestyle change and behavior modification, the underlying design of our digital solution is applicable to all of them. Only the medical, disease-specific components need to be modified and different markers have to monitored. Of note, the top four LMMCs are responsible for the great majority of premature mortalities due to NCDs in adults.

3- LifeJig is a Primary Care Quality Score Keeper: It uses a new scoring method that ties primary care performance to enhancement of patients’ physical and mental health. This is not a performance measure for administrative, cost or workflow efficacies but is a measure of success in reaching clinically meaningful and person-centric goals. The LifeJig score reflects a patient’s overall physical and mental health and when it improves, it increases their PCP’s score at the same time. The scores are updated automatically without any additional burden on PCPs who are notified and have access to reports in their portals.

4- Simulation Coaching **: Use of hypothetical scenarios in the form of simple quizzes (currently a total of 476 questions each with 4 multiple choice answers) that are sent to users on a daily basis. These clinically tested scenarios are extracted from real-life experience of patients and serve as “drills” to train users to make right choices in challenging or surprising situations. These quizzes are designed for:

• Goal Setting
• Self-Learning
• Self-Regulation
• Resilience and Relapse Prevention

Overtime, users’ responses determine the relevance and value of each quiz. Quizzes that are “too easy” or “too difficult” for any given community of users will be eliminated and replaced with those that generate scores with a broader spread in standard deviation. In addition, quizzes may also reveal patterns suggesting vulnerabilities, weaknesses or deficits in a community as a whole.

** R. Yavari MD: Simulation Coaching for Obesity and Diabetes, 2021

5- Life Skills: Over a decade of coaching, has allowed us to identify five life skills that are required for successful behavior modification and relapse prevention. These skills are practical in nature such as time management or they can be psychological like the ability to set boundaries in relationships. LifeJig identifies users’ deficits in these life skills. These life skills are universal, but their contexts vary in different countries. We have identified and tested six contexts that are present in all cultures, but their potency is personal and varies depending on the community. For example, setting boundaries (life skill) in interpersonal interactions (context) is linked to the fabric of a given community and may not be easy to adopt, while time management (life skill) may be more practical for reaching personal goals (context) related to lifestyle change. The machine learning capability built in our software application determines which contexts and life skills are more relevant for any specific demographic. Overtime, users’ responses determine the degree of relevance and prognostication value of each life skill.

6- Specialized AI-driven Health Bots: We have used GPT3 and GPT-J as NLP systems to pre-train our health bots to engage specifically for nutrition, exercise, life coaching chats. We will next add disease specific “educator bots” and a “triage” bot. These bots have been tested using WhatsApp as a messaging platform. We have designed the architecture, obtained cost estimates and potential partners to use a HIPAA compliant messaging system that allows data mining and machine learning.

These bots are intended to increase engagement and self-learning. They are not essential to our proposal and can be added as needed based on the community where LifeJig is implemented. While they DO NOT offer clinical care, they can support care with simple coaching tools such as calorie or carbohydrate counting, exercise tips and life skill-related guidance. In future versions, machine learning will allow bots to “get to know” the user and adjust life coaching tips accordingly.

• One Year Goals:

Launch the Spanish Version of our digital therapeutic Beyond Weight. This app which focuses on diabetes and obesity, is translated in its totality. Initial intake questionnaires, daily tips, quizzes, didactic educational materials, nutrition guidelines and recipes are all translated. We are waiting for additional funding to stand up this version. Next, we plan to launch Más Allá Del Peso in a Spanish speaking community in the US, Puerto Rico and Mexico. We believe our Spanish app will have significant impact in under resourced Latin and Hispanic communities.

Complete LifeJig Scoring and Coaching for all selected NCD tracks. We have selected seven NCD tracks - described elsewhere. The scoring system for these tracks is essentially identical to the current coaching program but must be expanded. The software for PCP scoring and reporting system is to be completed and tested in the coming year. Additional condition specific didactic materials and quizzes are also required. We believe that LifeJig will have a strong impact on the quality of primary care service in LMICs.

Grow User Subscription in the US and beyond. We understand that the B2C model is costly and time consuming. But we have had to collect user data from individual subscriptions to be able to reach out for enterprise accounts. We now have sufficient user data to present at trade shows and medical conferences in order to attract corporate or enterprise level interest. We hope to increase our app subscriber number to 1000 lives in the next year.

• Five Year Goals:

Scale. In the next 3 to 5 years, we plan to expand our user base to multiple LMCIs. We believe that with the English and Spanish versions of LifeJig we can have significant user base in Southeast Asia, Sub Saharan Africa and Latin America. We do however believe that translation to local languages will be important. In our experience with the Spanish version (which was done thru UpWork), this is not a difficult nor lengthy task. The number of people worldwide who are facing NCDs targeted by our digital therapeutic is so large that even if we capture a small fraction of our target demographic, we could still impact millions of lives and avoid loss of millions of healthy years.

LifeJig is born out of our digital therapeutic Beyond Weight, which we developed and launched for diabetes and obesity (two of the most common NCDs). This app has been commercially available since 2021 and has allowed us to achieve the following highpoints and show us how to measure our progress forward:

• Feasibility & Proof of Concept

Our app shows that it is feasible to design and launch a digital therapeutic in line with our current proposal. We believe that our approach can be easily extended to other chronic conditions that are modifiable by lifestyle change. Our digital therapeutic also shows that it is feasible to coach people in an automated fashion for an extended time – 6 months or longer - long enough to yield meaningful results.

- Subscription Model ($5.95 / month):

- Retention at 3 months 90%

- Retention at 6 months 61%

(We understand that this monthly subscription fee may be prohibitive in LMICs. Nonetheless, it reflects our solution’s retention rate as it is offered online.)

•  Analytics

- Weight loss (of initial body weight)

amongst users who completed 3 months:  4.5%

amongst users who completed 6 months:  7.2%

(The 6-month result is above the threshold set by the FDA of 5% as “significant weight loss” and is comparable to many in-person lifestyle change programs including the CDC’s PreventT2 Program.)

 Additional metrics under study:

- Percentage of users completing initial survey (108 questions)

- Percentage  of users answering 50% or more of daily quizzes

- Percentage of completers at 3 months and at 6 months

- Prevalence of medical conditions

- Use of medical screening services

- Life skill deficits and nature of challenges related to SDoH

- Knowledge of medical and health matters

Based on early results, we will measure our progress toward our impact goals with the following metrics:

• User Growth and Engagement Metrics

These metrics are straight forward: New Users, Retention, Completers, Answers to Quizzes, Number of Weekly Lessons Reviewed, etc.

• Success in Reaching Targeted Patient Goals

Condition-Specific Metrics: % Weight Loss for Obesity; BP for Hypertension, % drop in HbA1c for Diabetes; Hospital Admissions for Heart Failure or COPD, etc.

Quality of Sleep, Happiness Score, Lifestyle Enhancement and Behavior Modification

• Success in Increasing Quality of Primary Care

Number of PCPs participating, improved PCP Scores, Equity and Inclusiveness Measures

Even though challenges facing Primary Care in the USA are very different from those in LMICs, we can learn from recent modeling research in the US to adopt a theory of change that is meaningful for successful implementation and dissemination of our solution in target demographics.

The CMS Innovation Center (CMMI) was established in 2010 as part of the Affordable Care Act with the goal of transitioning the health system towards high-quality care while reducing cost. After ten years of research, testing 50 care models on 28 million lives, only six models met the criteria of quality and efficacy set by the Accountable Care Act in 2010. Thus, the National Academy of Medicine asked CMMI in 2020 to review and revise its policies and measures used to assess outcomes of primary care to achieve health goals, prevent illness, avoid pain and suffering in an inclusive, equitable manner. *

* https://innovation.cms.gov/str...

The transformation of healthcare in the US as defined and supported by policies of CMMI presents an actual framework for theory of change that we adhere to:

How to transform the current state of primary care to one that advances health equity, offers a person-centered care cognizant of social determinants of health, and is aligned with the priorities of local communities and environments.

To reach these targets, our theory of change must use appropriate metrics to measure intended outcomes: Implementation of high-quality primary care for tens of millions of people affected by NCDs in LMICs. These metrics must be adaptive to demographics, be flexible and sensitive to local contexts, and provide reliable, actionable, real-time results. We believe our solution includes such metrics and will not only yield reliable data to assess outcomes, but it will also offer pathways for transformation of Primary Care in LMICs.

The core technology components of our solution are relatively simple:

• LifeJig is a cloud-based software application developed in partnership with a leading healthcare SaaS company. This company has developed and launched Beyond Weight for us and will likely do the same for future versions. Beyond Care has full ownership rights to Beyond Weight and LifeJig.
• We currently use SMS and email for communication with users.
• User data are currently imported using Amazon Web Services and are analyzed using various analytical and machine learning tools, mostly developed using Python.
• AI bots were developed using a large-scale 6-Billion parameter general purpose transformer (GPT) to implement an NLP platform tasked with responding to common nutritional questions along with assisting in the implementation of LifeJig. We are currently awaiting funding to start deploying our SMS platform using artificial intelligence (AI) chatbots through a commercially available HIPAA-certified API. As noted previously, the use of AI-based chatbots is advantageous due to its capacity for scaling operations but is not critical in the near term for implementation of our solution in under-resourced communities.

• Individuals:

LifeJig is an automated digital coaching tool. Questions are sent to and answered by users populating data fields. Answers to these questions provide the basis of scoring users’ overall health, disease burden, lifestyle change, and behavior modification efforts and they assess individuals' SDoH.

• Primary Care Practitioners:

The primary care practitioner will assign each user to a lifestyle change coaching track most relevant to their main medical condition. PCPs will score the initial disease profile. Subsequently, PCPs will not be burdened with longitudinal data collection.

Dr. Yavari, the team leader and the founder of Beyond Care states: “I was sixteen when I was accepted to UC Berkeley. I flew to Berkeley CA, from Paris, France not knowing anyone. Before I knew it, I was surrounded by a community of diverse people in Bay Area. As a teenager who had just immigrated from Iran to France to California, I had a lot to learn and quick. I became very comfortable with diversity quickly and I have been ever since.” He continues “being trilingual and familiar with other languages, I have always been able to connect with people from all corners of the world. Years later, as a Howard Hughes Fellow, I worked in a Chinese lab at Yale for many years and I appreciated very much how Chinese scientists work so hard together and we became close friends.”

Beyond Care’s team is diverse and we are committed to embrace diversity in all its dimensions. As our team grows, we will make every effort to enrich our company by recruiting talented individuals with diverse life trajectories and cultures.

It is not easy to start a healthcare venture. The healthcare sector is not readily amenable to the “startup” business paradigm. Regulatory constraints, legacy systems and entrenched business models make it difficult to have rapid growth and sustainability. In addition, if the company’s product is a service – that is our case – it has to show positive results for beneficiaries, and outcomes take time. Only after our digital therapeutic is proven effective – that is users succeed in reaching targeted goals, will there be a real opportunity for scale. Therefore, our business model has to be flexible and adaptive to local environments. Ultimately our business will adopt a B2B model with various businesses such as telecommunication companies (ICTs), healthcare organizations such as hospitals or PCP clinics and even government-supported agencies and NGOs as our clients. However, our principal beneficiaries will always be individuals living with or at risk of NCDs in LMICs. We strongly believe in collaborative efforts, partnerships and diverse revenue lines.

The development and launch of Beyond Care’s primary product, our digital therapeutic app Beyond Weight has been self-funded. Beyond Weight is now commercially available and is currently generating a small revenue. The monthly hosting and finance transaction costs are minimal making the business model sustainable albeit early stage in the US.

To launch LifeJig in LMICs, we foresee significant operational costs and initially minimal revenue. The subscription model may not be possible in LMICs and organic growth will not be easy. We will need funding and partnerships to launch our product in LMICs and to reach sustainability. However, the worldwide prevalence of NCDs is so enormous that even if LifeJig captures a small market share, potential revenues will be tremendous. Since our lifestyle coaching service is automated, and our cost base is low, we believe we offer a value proposition to any for profit or nonprofit venture that aims to reach millions of people living in LMICs who are at risk of multi-morbidities.

Example 1:

Dr. Reza Yavari, the team leader, is also co-founder of Zillion Health a leading digital health SaaS and telecoaching company that currently has multiple coaching contracts with hospitals, employers and insurance companies. Before Zillion was able to gain traction and attract large clients, Reza was able to reach an agreement with Zillion to develop and launch Beyond Weight at no cost to Beyond Weight but in exchange for other services such as product and business development. Zillion does not have any equity in Beyond Care and has no ownership rights to our app – it is now simply a vendor collecting a monthly fees from subscriptions. This is an example of achieving a substantial milestone - developing and launching a digital therapeutic - with no funding.

Example 2:

Dr. Yavari is a Solve Alum. Ever since he won the competition in 2017, Reza has remained actively engaged with the Solve initiative: He has been present at almost every meeting in Cambridge and NYC; he has been a reviewer of applications for several years; and has tried to get funding through Solve Innovation Future (SIF) and other funding sources. When Beyond Care’s SIF funding application was rejected because the company’s business model was seen as B2C and therefore not fundable by SIF, Dr. Yavari was prepared to ask for a different kind of support: a partner. MIT Solve connected us with Luis Soenksen, PhD, who as a venture developer at the MIT was interested in joining us. Luis has so far developed our AI architecture in exchange for equity and is now a valued partner. This example shows that there are many paths to move forward.

These two examples show that we are committed to our vision, that we do not give up when facing obstacles or limitations. We truly believe in the global impact of what we are developing and will continue to pursue opportunities, grants, investments, partnerships and collaborations to reach our goal of successfully launching LifeJig in LMICs.