Impact of telehealth nutrition therapy on costs and utilization in type 2 diabetes & obesity

Impact of telehealth nutrition therapy on costs and utilization in type 2 diabetes & obesity | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Impact of telehealth nutrition therapy on costs and utilization in type 2 diabetes & obesity View ORCID Profile Priya V. Shanmugam , View ORCID Profile Rebecca N. Adams , View ORCID Profile Shaminie J. Athinarayanan , Adam J. Wolfberg , View ORCID Profile Jeromie Ballreich doi: https://doi.org/10.1101/2025.11.09.25339829 Priya V. Shanmugam a Research Department , Virta Health, Denver, CO PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Priya V. Shanmugam For correspondence: Priya.shanmugam{at}virtahealth.com Rebecca N. Adams a Research Department , Virta Health, Denver, CO PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rebecca N. Adams Shaminie J. Athinarayanan a Research Department , Virta Health, Denver, CO PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shaminie J. Athinarayanan Adam J. Wolfberg a Research Department , Virta Health, Denver, CO MD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jeromie Ballreich b Bloomberg School of Public Health, Johns Hopkins University , Baltimore, MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jeromie Ballreich Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Structured Abstract Importance Type 2 diabetes and obesity drive substantial morbidity and spending. Rigorous evidence on the impacts of digitally delivered lifestyle interventions on healthcare cost and utilization are critical to assessing their value. Objective Determine the impact of a telehealth-delivered individualized nutrition therapy (INT) program on per-member-per-month (PMPM) total cost of care and utilization over one and two years. Design Retrospective propensity score matched difference-in-differences analysis of cost and utilization outcomes over the study period of January 2016–March 2025. Setting US adults participating in a telehealth T2D and obesity management program. Participants Enrolled in INT for >1 day or had PCP visit for T2D or obesity during study period; had ≥12 months pre-index and ≥90 days post-index claims coverage (allowing ≤30-day gaps). Final matched sample: 6,580 participants and 6,580 controls (per arm: 3,819 with type 2 diabetes and 2,761 with obesity). Exposures Telehealth-delivered, continuous care integrating individualized carbohydrate-reduced nutrition support, clinician-guided medication management, health coaching, and remote biometric monitoring. Main Outcomes and Measures Outcomes included PMPM allowed inpatient, outpatient, and prescription medication costs, inpatient, emergency department, primary care, cardiology, and endocrinology visits. For the T2D cohort, PMPM spending and proportion of days covered for each T2D medication. Results Among 3,819 adults with T2D and 2,761 adults with obesity, program participation was associated with $240 and $256 PMPM reductions in the total cost of care at 12 months (−$230 and $189 over 24 months, all P<.001). In the T2D cohort, savings were driven by deprescription of SGLT2 inhibitors (66.8% reduction in PMPM cost), sulfonylureas (51.7%), insulin (43.9%), and GLP-1s (32.2%). In the obesity cohort, reductions accrued across inpatient, outpatient and prescription medication settings. Conclusions and Relevance In this large, real-world analysis, a nutrition-first digital care model was associated with sustained reductions in cost and utilization over 12–24 months, with immediate prescription medication cost reductions in the cohort with T2D and broader savings in the cohort with obesity. Together with prior clinical evidence, these findings suggest alignment of clinical effectiveness and cost reductions of a telehealth-delivered lifestyle intervention. Question Is participation in a telehealth-delivered individualized nutrition therapy program associated with changes in healthcare costs and utilization among adults with type 2 diabetes (T2D) or obesity? Findings In this retrospective cohort study of 13,000 matched adults, participation was associated with lower total cost of care at one year (−$240 and −$256 for adults with T2D and obesity respectively) and over two years (T2D: -$230; obesity: $-189). Among adults with T2D, reductions were driven by inpatient costs and all T2D medications, including GLP-1s. Among adults with obesity, reductions occurred across inpatient, outpatient, and prescription medication costs. Meaning Telehealth-delivered nutrition therapy may reduce healthcare spending for adults with T2D or obesity. Introduction Diabetes and obesity represent major interrelated public health challenges in the United States, impacting over 40 million 10 and 100 million 9 Americans respectively. Diabetes and obesity directly lead to substantial morbidity and mortality and increase the risk of myriad associated conditions including cancer, kidney disease, and cardiovascular disease. 18 , 41 These syndemic conditions further create a staggering economic burden, with combined direct healthcare costs exceeding $500 billion annually. 36 , 47 Despite significant advances in pharmacologic treatment options for both diabetes and obesity, including GLP-1 receptor agonists, real-world effectiveness is constrained by adverse effects, polypharmacy risks, coverage and affordability barriers that limit initiation and persistence. 2 , 5 , 20 , 21 , 23 , 27 , 30 , 35 , 39 , 40 Moreover, even under guideline-concordant therapy, substantial residual cardiometabolic risk remains. 19 Against a backdrop of rising pharmacy spend and payers’ sharpened focus on net budget impact, there is an urgent need to identify effective alternatives with tangible cost impacts. Intensive lifestyle intervention programs are effective 50 28 but limited by real-world clinical settings not designed for sustained behavior change, including limited access to registered dieticians and infrequent touchpoints. 1 , 14 – 16 Digital health platforms now serve millions of patients, representing a paradigm shift in chronic condition management 34 , 11 Wrap-around care models address gaps by delivering continuous, between-visit support integrating clinician-guided medication management, behavioral coaching, and timely specialist input. One such model is Virta Health’s Individualized Nutrition Therapy (INT) program, which is a telehealth-delivered, continuous remote care model integrating individualized carbohydrate-reduced nutrition support, clinician-guided medication management, and remote biomarker monitoring. In a prospective, controlled trial of the INT, adults with type 2 diabetes (T2D) experienced a 1.3% reduction in HbA1c and 12% weight loss at 1 year, while 94% of insulin users reduced or stopped insulin and sulfonylureas were completely eliminated. 25 Two-year outcomes showed a durable reduction in HbA1c (0.9%) and weight (10%) with medication deintensification, translating into higher remission compared with usual care. 3 Evidence on the total cost and utilization impact of digital wraparound care models – and thus, their comprehensive value proposition as a complement to traditional chronic condition management – is limited. This study evaluates the INT’s impacts on total cost of care (TCOC) and healthcare utilization using a retrospective claims-based analysis of over 13,000 patients over one and two years, leveraging a propensity score matched differences-in-differences (DID) study design to deliver rigorous evidence of the program’s causal impacts. Methods Study design We conducted a retrospective matched cohort study using a difference-in-differences design to estimate the impact of digitally delivered wraparound nutritional care offered as a complement to traditional primary care-based chronic condition management (UC) compared to UC alone. Data sources The study combined administrative data from the INT program with longitudinal claims data from the Komodo Healthcare Map, a nationally representative database of open and closed medical and prescription claims for over 300 million unique patients across commercial, Medicare and Medicaid plans. 31 Datavant privacy-preserving record linkage allows for identification of unique records across payers. The study used two extracts from the Komodo Healthcare Map for the study period of January 1, 2016 through August 20, 2025. The first contained claims for INT participants, and the second contained claims for 2.4 million patients that did not participate in INT and had either two T2D or two obesity diagnosis codes >30 days apart during the study period. The study investigators did not have access to the source data used to create the extracts. Only closed claims were used for the analysis, as described in Appendix 1. Clinic data includes program fees, participation length, baseline HbA1c and weight. All records were deidentified and compliant with United States patient confidentiality requirements, including the Health Insurance Portability and Accountability Act of 1996. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) and RECORD-PE guidelines for observational cohort studies. 17 , 32 Study population We compared two populations: (1) patients who enrolled in the INT between January 2017 and March 2025 (the INT arm) (2) patients who had a primary care (PCP) visit with T2D or obesity as a primary diagnosis but did not enroll in INT during the study period (the UC arm). INT and UC patients’ index date was defined as the first day of the month of INT registration or the PCP visit. The baseline period was defined as the year preceding the index date. Patients with obesity but without T2D are treated in the INT’s Sustainable Weight Loss (SWL) track, while patients with T2D, regardless of obesity, participate in the Diabetes Reversal (DR) track. We refer to INT patients in the DR track, and UC patients with a PCP visit for T2D management, as the “cohort with T2D”. The cohort with obesity includes INT patients in the SWL track and UC patients with a PCP visit for obesity management, reflecting their primary clinical indications. Patients with type 1 diabetes, heart failure, stage 4+ chronic kidney disease, end-stage renal disease, or acute psychosis during the baseline year were ineligible for the INT and excluded from both UC and INT arms. Patients without continuous closed claims during the baseline year or 90 days after index, allowing for gaps of up to 30 days, those aged <18 years at registration, or those with cancer or pregnancy during the baseline year were further excluded. In the T2D cohort, we excluded individuals without a baseline T2D diagnosis. In the cohort with obesity, we excluded individuals without a baseline obesity diagnosis, or with a diagnosis of T2D or use of T2D-indicated medications beyond GLP-1s and metformin. Figure 1 summarizes the study sample construction. Download figure Open in new tab Figure 1. Study sample construction flowchart Note: The continuous claims coverage period was defined as the period between the first and last day of closed-source medical and pharmacy claims data availability, allowing gaps ≤30 days, as detailed in Appendix 1. The index event was the first day of the month of either INT registration (for the INT arm) or a PCP visit with either T2D or obesity as a primary diagnosis (for the UC arm). The baseline period was defined as the 365 days preceding the index date. Exclusion conditions were defined by the presence of an ICD-10-CM code indicating Type 1 Diabetes, CKD Stages 4+ or ESRD, heart failure, acute psychosis, pregnancy or cancer during the baseline period. The cohort with T2D was required to have a diagnosis of T2D during baseline. The cohort with obesity was required to have a diagnosis of obesity and no diagnosis of T2D or use of SGLT2-i, sulfonylureas, thiazolidinediones, or DPP4s during baseline. Abbreviations: T2D: Type 2 diabetes; INT: Individualized Nutrition Therapy; UC: usual care; PCP: primary care physician; ICD-10-CM: International Classification of Diseases Volume 10 Condition Manual; CKD: chronic kidney disease; ESRD: end-stage renal disease; SGLT2-i: Sodium-glucose cotransporter-2 inhibitors; DPP-4s: dipeptidyl peitidase-4. Exposure The key exposure was enrollment in the INT. Comparator patients received UC and did not participate in INT during the study period. This active-comparator design isolates the impact of INT participation by comparing participants with patients initiating primary care, reducing confounding due to motivation or engagement. Patients are eligible for the INT if their health plan offers the program and if they have T2D, prediabetes, and/or obesity. Patients receive limited advertising about the INT and participate at no cost. Participation costs are paid by the health plan on an enrolled member-month basis. INT participants receive telehealth-delivered continuous remote care from licensed physicians and nurse practitioners integrating individualized very low-carbohydrate nutrition support (targeting generally <30g/day), medication management, health coaching, peer support, and regular biometric feedback via a mobile app. Medication management consists of guideline-informed initiation, titration, and deprescription of diabetes- and weight-related medications based on monitoring of glycemic control, weight, and other clinical indicators, consistent with standard of care. Appendix 2 reports participation measures for the INT arm. Outcomes We identified ten outcomes relevant to the management of T2D and obesity. Cost outcomes were measured per member per month (PMPM) and included costs of inpatient care, outpatient care, prescription medications, and total costs, defined as the sum of the previous three components. Costs excluded INT program fees, which are not captured in claims. INT costs were calculated using administrative data on monthly program prices scaled by enrollment length. Utilization outcomes were reported per 1,000 members per month (PKMPM) and included inpatient visits, emergency department (ED) visits, and outpatient evaluation and management visits with primary care providers (PCPs), endocrinologists, and cardiologists. For the T2D cohort, nine additional cost outcomes included costs for each T2D medication (metformin, thiazolidinediones, GLP-1s, SGLT2is, sulfonylureas, DPP4s, and insulin) separately, all T2D medications combined, and all non-T2D medications combined. Seven additional utilization outcomes included the proportion of days covered for each T2D medication. All outcomes were measured through the end of each patient’s closed claims coverage period, up to twenty-four months post-index. Covariates The index date was the first day of the month of registration for INT (INT arm) or PCP T2D or obesity management visit (UC arm). Baseline covariates included age at index date, sex, race, comorbidities (high cholesterol, hypertension, cardiovascular disease, chronic kidney disease, liver disease, and smoking), and baseline medication use (all T2D-indicated medications and lipid- and blood pressure-lowering medications). Comorbidities and prescription medication use during the baseline year were identified by the presence of at least one claim with a corresponding ICD-10 code (Appendix 3) or medication name (Appendix 4). Statistical analysis Matched control group construction For the cohorts with T2D and obesity separately, matched control groups were constructed using 1:1 nearest neighbor matching on a propensity score estimated based on sex, race, five-year age groups, payer type, US region, binary indicators for baseline comorbidities and prescription medication use, and categorical indicators of baseline inpatient, outpatient, and prescription medication cost quartiles. For the cohort with obesity, the propensity score included binary indicators of baseline use of GLP-1s, other antiobesity medications, and metformin. For the cohort with T2D, the propensity score included baseline obesity and use of each T2D-indicated medication. Matching was conducted using the MatchIt package in R. 26 Estimation framework We estimated difference-in-differences regression models including indicators for INT participation, the post-index period, and their interaction. Individual and calendar-month fixed effects were included to account for time-invariant individual characteristics and secular trends. The interaction term estimated the differential change in outcomes associated with INT participation. Standard errors were clustered at the individual level. The estimation sample was limited to the twelve months prior to, and twelve or twenty-four months following, each patient’s index date. We estimated the model for the cohort with T2D and cohort with obesity separately. For the cohort with T2D, we also estimated the model for the secondary outcomes specified above. In this study, individuals in the comparison arm initiate a comparator intervention (PCP-based chronic condition management). In both the intervention (INT) and usual care (UC) arms, the post-period is defined relative to each patient’s initiation date. Accordingly, we used a classical 2×2 difference-in-differences (DID) specification with indicators for study arm, post-period, and their interaction. We did not use a traditional two-way fixed-effects (TWFE) DID model, in which the comparison group never receives a comparator treatment and therefore lacks an analogous index date and pre-post timing. 22 The DID model’s key assumption is that in the absence of treatment, outcomes would have evolved similarly over time in the INT and UC arms. We assessed parallel trends for each outcome using (1) tests of differential baseline linear trends and (2) visual assessment of pre-intervention trends using an event-study specification estimating monthly coefficients relative to the month before index. Results Baseline sample characteristics 6,580 INT and 94,233 UC patients met the study eligibility criteria. The matched study sample contained 3,819 patients per arm (cohort with T2D) and 2,761 patients per arm (cohort with obesity). Table 1 shows the baseline characteristics of each cohort. The cohort with T2D averaged 55 years old at index, 52% male, and 49% white. The cohort with obesity averaged 48 years old, 29% male and 52% white. Baseline medications and comorbidities were comparable between the INT and UC arms. Appendices 5 and 6 describe the characteristics of the INT and UC arms and propensity score distribution before and after matching for each cohort. View this table: View inline View popup Table 1. Study sample characteristics for matched cohorts with obesity and T2D Impacts Table 2 reports DID estimates by cohort. The intervention was associated with significantly lower healthcare utilization and costs in each cohort. Figure 2 reports event-study DID estimates for the twelve pre- and post-intervention months, confirming the baseline parallel trends assumption. Download figure Open in new tab Figure 2. Event-study DID estimates for key outcomes Note: The figure plots coefficients for the interaction terms between INT participation and each month indicator for the twelve months before and after the index date, with the month prior to the index date as the reference category. Month 0 is the first month that patients either register for the INT or have a PCP visit for T2D/obesity management. Blue estimates indicate treated months; red estimates indicate pre-treatment months. Estimates are derived from a regression model of individual monthly outcomes adjusted for individual and calendar-month fixed-effects. Standard errors are clustered at the member level. Abbreviations: DID, differences-in-differences; T2D, type 2 diabetes; INT, individualized nutrition therapy; PCP, primary care physician. View this table: View inline View popup Table 2. DID estimates of cost and utilization impacts for cohorts with T2D and obesity Cohort with T2D Cost In Year 1, total health care costs were reduced by $240 PMPM (95% CI, $123–$357), corresponding to a 21.1% reduction relative to baseline. The reduction was driven by inpatient costs ($98 PMPM [95% CI, $34–$162]) and T2D-indicated medications ($145 PMPM [95% CI, $128–$161]). Appendix 7 reports impacts by T2D medication. Cost reductions were observed across all T2D medication classes, including SGLT2 inhibitors ($54 PMPM, or a 66.8% reduction relative to baseline), GLP-1s ($59, or 32.2%), and insulin ($22; 43.9%), with smaller, statistically significant reductions for metformin, DPP-4 inhibitors, and sulfonylureas. Reductions in the proportion of days covered were also observed for all T2D medications, including SGLT2 inhibitors (−55.5%), GLP-1s (−23.3%), and insulin (−30.8%). Follow-up averaged 10.8 months in year one and 17.0 months over two years. Over two years, total costs were reduced by $230 PMPM, indicating sustained impacts. Table 3 compares total program fees against estimated cost reductions over one and years. Per-member net savings were $929 in year one, and $1,374 over two years. View this table: View inline View popup Download powerpoint Table 3. Net savings estimates Utilization In Year 1, participants had 3.2 (95% CI 1.4-5.0) fewer inpatient visits, 4.1 (2.1-6.0) fewer endocrinologist visits, and 60.6 (48.8 – 72.4) fewer PCP visits per 1,000 members per month (PKMPM). Over two years, participants had reductions of 3.4 (1.8-5.1) inpatient, 2.8 (1.0-4.8) endocrinologist, and 50.2 (38.7-61.6) fewer PCP visits PKMPM, corresponding to 40.8 fewer hospitalizations per 1,000 members annually, 19.4% fewer PCP visits and 28.6% fewer endocrinologist visits relative to the INT arm’s baseline. Impacts on ED and cardiologist visits were not statistically significant. Cohort with obesity Cost In our standard DID specification, there was a significant difference in the baseline anti-obesity medication cost trend, driven entirely by a larger increase in GLP-1 costs in the UC arm. To mitigate the impact of the differential trend, for each outcome, we estimated the baseline trend difference, detrended the outcome through year two, and estimated the model on the detrended outcome. Results for unadjusted outcomes are presented in Appendix 8. Additionally, substantial attrition was observed between years one and two in the cohort with obesity: mean follow-up was 10.0 months in the first post-index year, but 13.3 months across two years. In Year 1, total healthcare costs were reduced by $256 PMPM (95% CI, $138-375), reflecting a 29.7% reduction from baseline. The reduction was driven by inpatient ($117 PMPM [$58-175]), outpatient ($99 [$7-192]) and prescription medications costs ($40 PMPM [$14-66]). Over two years, total costs were $189 (77-301) PMPM lower than baseline, indicating sustained cost reductions. Table 3 compares total program fees against estimated savings, suggesting $1,374 net savings in year one and $1,395 over two years. Utilization Baseline trend estimates for low-frequency utilization outcomes (e.g., inpatient or ED visits) are imprecise, and detrending these outcomes may amplify noise or lead to poorly extrapolated outcomes. We therefore present utilization outcomes unadjusted. Participants had 4.8 (95% CI 2.8-6.8) fewer inpatient, 5.3 (1.4-9.2) fewer ED, and 74.7 (59.3-90.1) fewer PCP visits PKMPM. Additionally, participants had 2.5 (0.4-4.5) and 2.6 (0.9-4.2) fewer cardiologist and endocrinologist visits respectively. Over two years, participants had 3.8 (1.9-5.6) fewer inpatient and 65.6 (51.0-80.1) fewer PCP visits PKMPM, corresponding to 45.6 fewer hospitalizations per 1,000 members annually and 23.7% fewer PCP visits relative to baseline. Discussion In this large claims-based study of 13,000 adults with T2D and obesity, participation in a clinically effective telehealth nutrition program was associated with lower total cost of care and utilization, generating approximately $1,400 per member in net savings over two years. These findings suggest that intensive digital nutrition care can achieve both clinical effectiveness and meaningful cost reductions. Impacts of the INT differed by clinical indication in ways consistent with the care model. Among participants with T2D, savings were driven by reductions in T2D medication spending, consistent with the rapid glycemic improvements and consequent deprescription observed in prior clinical studies. 25 Although digital lifestyle interventions have demonstrated glycemic benefits 33 , 12 prior claims-based economic evaluations often report modest or short-lived savings. 13 , 42 , 46 The observed 12- and 24-month reductions in pharmacy and inpatient spending suggest that pairing traditional chronic condition management with telehealth-delivered nutritional care results in significant cost reductions. In the cohort with obesity, savings accrued across a broader set of cost domains, consistent with evidence that digital support can augment clinical outcomes achieved through pharmacotherapy. 37 Economic evaluations of prevention and weight management programs remain limited and mixed, with many reporting minimal cost reductions despite clinical benefit. 4 , 6 , 8 , 45 Multiple evaluations of GLP-1s, which cost $12,000–$16,000 annually per patient 38 , have shown no cost reductions within one to five years. 48 , 7 , 49 , 24 The INT compared favorably, generating $1,600 per member in net savings within one year. The study has limitations. First, attrition through year two was substantial, particularly in the obesity cohort. Conditioning on follow-up could limit the generalizability of the results, as follow-up is determined by employment duration, or generate immortal time bias if INT participation affected employment duration and insurance coverage. Moreover, while healthcare cost reduction estimates were impacted by attrition, INT participation costs were derived from non-claims sources and thus not impacted by attrition. While this asymmetry likely places downward pressure on two-year estimates, we report estimates across both timeframes, noting that they reflect both real-world insurance churn and consequent data constraints inherent to evaluating employer-sponsored programs in commercially insured populations. Second, digitally delivered lifestyle programs are widespread: 85% of large employers reported offering disease management programs in 2023. 29 Because such programs paid through direct employer-vendor contracts and not captured in claims data, we cannot rule out potential contamination of the UC arm, which would bias estimated effects toward zero. Third, residual confounding is possible in a nonrandomized observational study despite propensity score matching and a DID design. We present outcome-specific and joint baseline parallel trends tests, event-study specifications, and detrended alternate specifications to mitigate the impacts of unobserved confounding. Fourth, the study excluded 15.3% of INT participants with T2D and 57.6% with obesity who lacked qualifying claims-based diagnoses during the baseline year, consistent with known under-recording of obesity in claims data. 44 Linked claims and EHR data are needed to assess the generalizability of these findings to the broader cohort, particularly for INT participants with BMI-defined obesity described in Appendix 9. Fifth, the study population was largely commercially insured. While our results are consistent with evaluations of the INT within the Veterans Health Administration, 43 broader evidence is warranted given recent expansions in Medicare reimbursement for digitally delivered care programs. Conclusion In this claims-based, real-world evaluation leveraging causal inference methods, enrollment in a telehealth nutritional care program was associated with significant reductions in healthcare utilization and costs over one and two years among adults with T2D and obesity. Reductions were driven by decreases in inpatient and pharmacy spending in the cohort with T2D and decreases in all spending categories in the cohort with obesity. These results suggest that telehealth-delivered nutritional care may be an effective method to improve outcomes and reduce healthcare costs in populations with metabolic disease. Data Availability The data underlying this article was provided by the third party, Komodo Health, under license and cannot be shared publicly. The source data for this study were licensed by Virta Health from Komodo Health and hence may not be shared publicly. Funding No funding supported this work. Competing interests PVS, RNA, SJA, and AJW are employees of Virta Health and hold stock or stock options in the company. JB received consulting fees from Virta Health for his contribution to the work. Data Sharing Statement The data underlying this article was provided by the third party, Komodo Health, under license and cannot be shared publicly. The source data for this study were licensed by Virta Health from Komodo Health and hence may not be shared publicly. Footnotes The study design was revised on two fronts. First, we include all INT enrollees regardless of their length of participants, in an intent-to-treat design. Second, we now index UC patients to a PCP visit for T2D/obesity management, rather than a randomly selected pseudo index date, to better compare INT participants to an active comparator group engaging in standard care. Third, we provide a more detailed discussion of the program's net savings. References 1. ↵ AHA . 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