---
title: "Participation in a low‑intensity health assessment program linked to lower diabetes risk in older"
id: "plos-medicine-0-association-of-a-multiple-risk-factor-assessment-and-intervention-program-with"
canonical_url: "https://medichelpline.com/clinical-feed/plos-medicine-0-association-of-a-multiple-risk-factor-assessment-and-intervention-program-with"
content_type: "clinical_feed_article"
specialty: "Endocrinology"
source_name: "PLOS Medicine"
source_url: "https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005043"
published_at: "2026-09-21T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Participation in a low‑intensity health assessment program linked to lower diabetes risk in older
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-medicine-0-association-of-a-multiple-risk-factor-assessment-and-intervention-program-with
- **Specialty:** [Endocrinology](https://medichelpline.com/clinical-feed/endocrinology.md)
- **Primary Source:** PLOS Medicine
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005043)
- **Published At:** 2026-09-21T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This population‑based matched cohort study evaluated whether participation in the **Healthy Ageing Initiative (HAI)**, a low‑intensity comprehensive health assessment and motivational counseling program for 70‑year‑olds in Umeå, Sweden, was associated with incident **diabetes**. - The study included 6,018 HAI participants and 57,543 matched controls (matched 1:10 on birth year, sex, and educational level) after excluding individuals with prevalent diabetes; follow‑up extended through December 31, 2022. - Incident diabetes was ascertained via nationwide registers using first recorded specialist diagnosis (ICD‑10 E11) or first dispensed glucose‑lowering medication (ATC A10); censoring occurred at death or end of follow‑up. - Mean follow‑up was 4.9 years (SD 2.9) in the HAI group and 4.8 years (SD 2.9) in controls; diabetes occurred in 335 (5.6%) participants versus 3,919 (6.8%) controls. - Participation was associated with a lower hazard of incident diabetes: adjusted hazard ratio **0.76** (95% CI 0.68–0.85; p < 0.001). The association remained stable over time in analyses presented. - Absolute risk reduction was estimated at **1.46 percentage points** (95% CI 0.91–1.98; p < 0.001) at 5 years and **3.42 percentage points** (95% CI 2.13–4.75; p < 0.001) at 10 years, based on Royston–Parmar survival models with bootstrap CIs. - Sensitivity analyses included competing‑risk Fine–Gray models treating death as competing event; subgroup analyses showed broadly consistent results across examined groups. - The HAI program involved two structured assessment visits with individualized feedback and motivational counseling delivered in routine care, without structured long‑term follow‑up; individuals with concerning findings were advised to consult their general practitioner. - Important limitations: observational matched design cannot establish causality; voluntary participation may introduce selection bias and a healthy‑volunteer effect; residual confounding is possible despite adjustment for a range of baseline covariates from registers. - The authors conclude that a scalable, low‑intensity prevention program embedded in routine care was associated with lower **diabetes** incidence among older adults, but more robust evidence is needed to confirm causality and to inform policy about population‑wide prevention strategies.
## Clinical Analysis & Structured Key Points
Association of a multiple risk factor assessment and intervention program with risk of diabetes: A population-based matched cohort study | PLOS Medicine Article Authors Metrics Comments Media Coverage Reader Comments Figures ? This is an uncorrected proof. Figures Abstract Background Type 2 diabetes incidence remains high among older adults and contributes substantially to morbidity and healthcare burden. Most evidence supporting diabetes prevention derives from intensive lifestyle interventions targeting high-risk individuals under controlled trial conditions. The effectiveness of low-intensity, population-based prevention programs embedded in routine care remains uncertain. We evaluated whether participation in a comprehensive health assessment and counseling program was associated with reduced incidence of diabetes in older adults. Methods and findings We conducted a population-based matched cohort study including 70-year-old residents of Umeå municipality, Sweden, enrolled between 2012 and 2022 and followed through December 2022. The intervention consisted of a comprehensive health assessment followed by individualized motivational counseling, without structured long-term follow-up. Participants were matched 1:10 to controls from the general Swedish population by birth year, sex, and educational level. Individuals with prevalent diabetes were excluded. The primary outcome was incident diabetes, defined as a first recorded diagnosis in specialist care and/or first dispensed glucose-lowering medication, identified through nationwide health and prescription registers. In total, 6,018 participants and 57,543 matched controls were included. During a mean follow-up of 4.9 years (standard deviation 2.9) in the intervention cohort and 4.8 years (standard deviation 2.9) in the control cohort, diabetes occurred in 335 (5.6%) participants and 3,919 (6.8%) controls. Participation was associated with a lower risk of incident diabetes (adjusted hazard ratio 0.76; 95% confidence interval [0.68,0.85]; p < 0.001). The association remained stable over time. The absolute risk reduction was 1.46 percentage points (95% confidence interval [0.91,1.98]; p < 0.001) at 5 years and 3.42 percentage points (95% confidence interval [2.13,4.75]; p < 0.001) at 10 years. Results were broadly consistent across the examined subgroups. The observational study design limits certainty regarding the intervention’s role in the observed association, and voluntary participation in the program may have introduced selection bias, healthy-volunteer effect, and residual confounding. Conclusions In this population-based cohort study, participation in a low-intensity preventive health program delivered in routine care was associated with a lower incidence of diabetes among older adults. However, the observational study design limits evidence on the intervention’s role in the observed association, and more robust evidence is needed. Nevertheless, these findings suggest that scalable, population-wide prevention strategies may complement intensive high-risk approaches in addressing diabetes in aging populations. Author summary Why was this study done? Type 2 diabetes is common in older adults and contributes substantially to morbidity and healthcare costs. Most evidence for diabetes prevention comes from intensive lifestyle programs targeting selected high-risk individuals in clinical trials. It remains unclear whether low-intensity, population-based prevention programs delivered in routine healthcare settings can reduce diabetes risk in the general older population. What did the researchers do and find? We conducted a population-based matched cohort study including 6,018 participants in a preventive health assessment and counseling program and 57,543 matched controls. Participants were 70-year-old residents of Umeå municipality, Sweden, free from diabetes at baseline. During a mean follow-up of 4.8 years, participation in the program was associated with a 24% lower risk of developing diabetes. The absolute difference in diabetes risk between groups increased over time and was consistent across most examined subgroups. What do these findings mean? In this real-world setting, participation in a low-intensity preventive health program was associated with lower diabetes incidence among older adults. The findings suggest that scalable prevention strategies embedded in routine care may contribute to reducing diabetes burden at the population level, where even modest reductions in individual risk could translate into substantial public health benefits. The observational study design does not allow causal conclusions, and voluntary participation may have resulted in a healthier group of participants than non-participants. Citation: Bergman E, Nordström A, Nyberg L, Nordström P (2026) Association of a multiple risk factor assessment and intervention program with risk of diabetes: A population-based matched cohort study. PLoS Med 23(9): e1005043. https://doi.org/10.1371/journal.pmed.1005043 Academic Editor: Andre P. Kengne, South African Medical Research Council, SOUTH AFRICA Received: March 14, 2026; Accepted: September 10, 2026; Published: September 21, 2026 Copyright: © 2026 Bergman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The individual-level data underlying this study cannot be made publicly available or transferred directly by the authors because they contain sensitive personal information and are subject to the European Union General Data Protection Regulation, Swedish data protection and confidentiality legislation, ethical approval, and data-use agreements. Eligible researchers may apply separately to the relevant data custodians. Applications generally require an eligible research institution, a defined research project, specification of the requested population and variables, relevant ethical approval, and approval following legal and confidentiality review by the respective data custodian. Access is not guaranteed. Health-register data can be requested from the Swedish National Board of Health and Welfare at https://bestalladata.socialstyrelsen.se/data-for-forskning/ ( mikrodata@socialstyrelsen.se ). Sociodemographic microdata can be requested from Statistics Sweden at https://www.scb.se/vara-tjanster/bestall-data-och-statistik/mikrodata/ ( mikrodata@scb.se ). Requests concerning access to the HAI research data should be submitted to the Office of the Registrar at Uppsala University ( registrator@uu.se ). The authors are not permitted to redistribute the linked individual-level dataset and had no special access privileges unavailable to other eligible researchers. The code used for the statistical analyses and creation of figures is available from and has been permanently archived in Zenodo with the URL: https://doi.org/10.5281/zenodo.21820677 , and DOI: https://doi.org/10.5281/zenodo.21820677 . The code is provided without the underlying individual-level data, which must be requested separately as described above. Funding: This work was supported by a grant from King Gustaf V and Queen Victoria’s Foundation (URL: https://frimurarestiftelsen.se/ ) awarded to PN. The Foundation does not assign grant numbers to its awards. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The author received no salary support from the funder. Competing interests: The authors have declared that no competing interests exist. Abbreviations: CI, confidence interval; CVD, cardiovascular disease; DPS, Diabetes Prevention Study; HAI, Healthy Ageing Initiative; HR, hazard ratio; PAF, Population attributable fraction; SD, standard deviation Introduction Diabetes mellitus is a major public health challenge and is highly prevalent among older adults [ 1 ]. It contributes substantially to cardiovascular morbidity, functional decline, and healthcare utilization worldwide [ 2 – 4 ]. In Sweden and many other countries [ 5 ], diabetes incidence increases with age before plateauing in the oldest age groups [ 6 ]. Preventing diabetes in aging populations is therefore an important clinical and public health priority. Strong evidence supports lifestyle modification for diabetes prevention, primarily from randomized controlled trials targeting individuals at elevated risk through intensive, structured programs [ 7 – 11 ]. Although these interventions have demonstrated substantial reductions in diabetes incidence under controlled conditions, their intensity, resource requirements, and selective inclusion criteria may limit scalability and sustainability in routine healthcare setting [ 12 – 14 ]. Whether lower-intensity, population-based prevention strategies embedded in routine care can influence diabetes incidence in the general older population remains uncertain. Population-wide preventive initiatives implemented in European and Nordic settings have reported improvements in lifestyle behaviors and cardiometabolic risk profiles [ 15 – 18 ]. However, evidence regarding their association with hard clinical outcomes such as incident diabetes is limited, and older adults are frequently underrepresented [ 15 ]. Moreover, little is known about whether potential associations are consistent across demographic and clinical subgroups in real-world healthcare contexts [ 19 ]. The Healthy Ageing Initiative (HAI) is a population-wide preventive health program offered to all 70-year-olds within a defined Swedish healthcare region [ 20 ]. The program includes a comprehensive health assessment followed by individualized motivational counseling delivered within routine care, without structured long-term follow-up. Previous analyses have shown that participation in this program was associated with reduced cardiovascular disease (CVD) incidence [ 21 ]. Whether participation is also associated with reduced diabetes incidence has not been evaluated. Using a population-based matched cohort design with nationwide register follow-up, we examined whether participation in this low-intensity, routine-care prevention program was associated with incident diabetes among older adults. We also assessed whether any observed association was consistent across key baseline characteristics. Methods Study design and setting We conducted a population-based matched cohort study examining the association between participation in the HAI, a primary prevention program in Umeå municipality, Sweden [ 20 ], and incident diabetes. Participants in the program were matched to controls from the general Swedish population. Follow-up was performed through linkage to nationwide registers, including the National Patient Register, the Prescribed Drug Register, and the Cause of Death Register [ 22 – 24 ], which provide near-complete national coverage of diagnoses, dispensed medications, and mortality. The study period spanned from June 1, 2012, to December 31, 2022. The study was conducted and reported in accordance with Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines ( S1 Checklist ). Healthy Ageing Initiative cohort. The intervention cohort comprised 54% of 70-year-old residents invited within the municipality during the study period. Participation was open to all eligible individuals, with no exclusion criteria applied. The program included two structured health assessment visits conducted within routine care, during which lifestyle factors and cardiometabolic risk markers were evaluated. It was designed as a pragmatic, low-intensity program that could be implemented in routine clinical practice to target the major modifiable risk factors for CVD and diabetes at a population level. Participants received individualized feedback and motivational counseling aimed at promoting lifestyle changes relevant to cardiovascular and metabolic disease prevention. No structured long-term follow-up was provided within the program, and participants subsequently returned to ordinary care. Individuals with findings suggestive of undiagnosed conditions were advised to consult their general practitioner. A detailed description of the program is provided in S1 Appendix , and similar descriptions have previously been published [ 21 , 25 ]. The participants included in the program were generally healthier, with a lower prevalence of CVD and diabetes, than Umeå residents who did not participate [ 21 ]. Control cohort. A control cohort was drawn from the general Swedish population by Statistics Sweden and individually matched 1:10 to HAI participants exactly on birth year, sex, and educational level [ 26 ]. Controls were required to be alive and free of diabetes at the index date corresponding to their matched participant and were assigned the same index date to ensure comparable follow-up time. Exclusion criteria. Individuals in both cohorts were excluded if they had prevalent diabetes at baseline, defined as a recorded diagnosis of diabetes (International Statistical Classification of Diseases and Related Health Problems, 10th Revision, ICD-10 codes E10 and E11) in the National Patient Register or a dispensed prescription of glucose-lowering medication (Anatomical Therapeutic Chemical Classification System, ATC code A10) before or on the index date. Individuals with missing data on matching variables were also excluded. Outcome definition The primary outcome was incident diabetes during follow-up. Incident cases were identified through nationwide register linkage to the National Patient Register and the Prescribed Drug Register. Diabetes was defined as a first recorded diagnosis in specialized inpatient or outpatient care (ICD-10 code E11) or a first dispensed prescription of glucose-lowering medication (ATC code A10), whichever occurred first. The date of the first qualifying diagnosis or prescription was used as the event date. Participants were censored at death, identified through the Cause of Death Register, or at the end of follow-up. Covariates Baseline covariates were specified prior to data analysis, based on subject-matter knowledge, and the availability of consistently recorded variables in the nationwide registers. Age, sex, and educational level were accounted for through matching. Additional covariates were selected to capture baseline differences in cardiometabolic morbidity, overall health and functional status, and medication use that could confound the eventual association with intervention and diabetes risk. Country of birth (born in Sweden versus elsewhere) was used as a proxy for broader sociodemographic health-related differences. Home care services were included as an indicator of functional dependency. Pre-existing diagnoses, identified using ICD-10 codes, included myocardial infarction (I21), stroke (I61, I63, I64), angina pectoris (I20), renal disease (N17-N19), chronic obstructive pulmonary disease (J44), cancer of the colon and rectum (C18, C20), breast cancer (C50), mental and behavioral disorders due to alcohol use (F10). Medication covariates, identified using ATC codes, included antihypertensive medications (C03, C07-C09), lipid-lowering agents (C10), antidepressants (N06A), and prednisolone (H02AB06). All covariates were ascertained on or before the index date. No data-driven variable selection procedures were applied. Statistical analysis Follow-up time was calculated from the index date (defined as the second visit within the HAI program for participants and the corresponding assigned date for controls) until the first occurrence of incident diabetes, death, or end of follow-up (December 31, 2022), whichever occurred first. Descriptive statistics were used to summarize baseline characteristics. The unadjusted association between participation in the HAI program and incident diabetes was estimated using Cox proportional hazards regression models stratified by matched set to account for individual matching. The proportional hazards assumption was assessed using Schoenfeld residuals. Adjusted hazard ratios (HRs) and cumulative incidence functions were estimated using Royston–Parmar flexible parametric survival models (with splines at the 25th, 50th and 75th percentiles of uncensored log survival times) with robust standard errors clustered by matched set. Royston–Parmar models provide smooth estimates of the underlying survival function, allowing direct estimation of adjusted cumulative risks and absolute risk differences. The Royston–Parmar models can also accommodate potential violations of the proportional hazards assumption (i.e., time-varying intervention effects). Absolute risk estimates and confidence intervals (CIs) were obtained through refitted bootstrap resampling. Competing-risk sensitivity analyses were performed using Fine–Gray subdistribution hazard models treating death as a competing event to evaluate the robustness of the observed association and to estimate the cumulative incidence of diabetes. Effect modification was assessed by including interaction terms between intervention status and predefined baseline characteristics. Population attributable fractions (PAF) were estimated using model-based predictions to quantify the proportion of incident diabetes statistically attributable to selected baseline risk factors, assuming causal associations. A general research plan, including the planned analytical approach, was agreed upon within the research team before data analysis, although it was not formally documented. The analyses reported were planned before data analysis, and no additional data-driven analyses were performed. Statistical analyses and creation of figures were performed in R (version 4.5.2; R Foundation for Statistical Computing, Vienna, Austria) in RStudio (version 2025.09.2+418; Posit Software, PBC, Boston, MA, USA). Ethical approval and data handling The HAI program and the present study were conducted in accordance with the Declaration of Helsinki and approved by the Swedish Ethical Review Authority (Number 07-031 with extensions). All participants in the program provided written informed consent to participation in the health program and to subsequent research based on the collected outcome data. Individual informed consent was not obtained for the control population as they were identified through nationwide administrative registers and available to the researchers only as pseudonymized register data, as approved by the Swedish Ethical Review Authority. All data handling complied with applicable data protection regulations. Generative artificial intelligence (ChatGPT, GPT-5; OpenAI, San Francisco, CA, USA) was used to assist with the development of statistical scripts in R and to improve clarity and language in the text. Following the use of this tool, the authors critically reviewed, edited and verified all content. The authors retain full responsibility for the study design, analyses, interpretation of the data, and the final manuscript. Results Study population The exclusion process resulted in the exclusion of 626 (9.4%) HAI participants and 8,898 (13.4%) controls with prevalent diabetes at baseline. An additional 5 (<0.1%) HAI participants and 41 (<0.1%) controls were excluded due to missing educational data; see Fig 1 . The final study population comprised 63,561 individuals, including 6,018 HAI participants and 57,543 matched controls. Mean follow-up was 4.9 years (standard deviation [SD] 2.9) in the HAI cohort and 4.8 years (SD 2.9) in the control cohort, with a total follow-up time of 29,714 and 276,761 person-years, respectively. During follow-up, incident diabetes occurred in 335 (5.6%) HAI participants and 3,919 (6.8%) controls. Baseline characteristics are presented in Table 1 , with additional characteristics provided in S1 Table . Download
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