The lack of effective, scalable solutions for lifestyle treatment is a global clinical problem, causing severe morbidity and mortality. We developed a method for lifestyle treatment that promotes self-reflection and iterative behavioral change, provided as a digital tool, and evaluated its effect in 370 patients with type 2 diabetes . Users of the tool had reduced blood glucose, both compared with randomized and matched controls , as well as improved systolic blood pressure, body weight and insulin resistance. The improvement was sustained during the entire follow-up (average 730 days). A pathophysiological subgroup of obese insulin-resistant individuals had a pronounced glycemic response, enabling identification of those who would benefit in particular from lifestyle treatment. Natural language processing showed that the metabolic improvement was coupled with the self-reflective element of the tool. The treatment is cost-saving because of improved risk factor control for cardiovascular complications. The findings open an avenue for self-managed lifestyle treatment with long-term metabolic efficacy that is cost-saving and can reach large numbers of people. Structured lifestyle treatment is currently limited by the associated need for large healthcare resources. Digital tools could enable clinical utility but most solutions require coaching, intensified healthcare activities or user fees, which hinder broad application. In view of the escalating numbers of afflicted people, also in low- and middle-income countries, it is necessary to find new ways to support lifestyle self-management and complement drug treatment and resource-intense lifestyle programs4. This is, however, currently hampered by several important knowledge gaps5.Lifestyle-related diseases such as type 2 diabetes are major causes of death and disability and put strains on health expenditures world-wide8. Recent meta-analyses have shown declining metabolic response to digital lifestyle interventions after six months and most studies to date lack investigation of the effective behavior change techniques and have applied only limited measures to adjust for potential confounders11. Second, digital tools are often combined with coaching or intensified healthcare contacts, making it difficult to specifically assess the effect of the digital component and apply the results to a broad range of settings11. In fact, one of the major limitations to structured lifestyle support, whether traditional or digital, is that it currently requires increased efforts by healthcare professionals or costs for the individual patient, and less than one out of ten individuals with diabetes take part in such programs3. Third, studies from individuals at risk for diabetes have indicated an impact of BMI and insulin resistance on the effect of exercise, but how the individual pathophysiology influences the response to lifestyle treatment in patients with manifest type 2 diabetes remains unknown13. This is pertinent to address, especially in light of recent data, emphasizing the clinical variability of type 2 diabetes and highlighting four clusters of patients with different disease characteristics14. Such information could potentially help tailor lifestyle programs to those who are most likely to benefit8.First, data on long-term effects (>6 months) are scarce14. Finally, we aimed to assess the effective elements of the treatment by natural language processing using machine-learning.Here we developed and evaluated a self-managed lifestyle tool that is scalable and does not require additional healthcare resources. We hypothesized that HbA1c, reflecting long-term blood glucose, would decrease in patients with type 2 diabetes exposed to the tool, both compared to randomized wait list controls and matched controls. We also hypothesized that the tool would be particularly effective in one of the recently identified clusters, termed Mild Obesity-related Diabetes (MOD), which represents obese insulin-resistant patients and encompasses 22–30% of individuals with type 2 diabetesBefore constructing the digital treatment tool, we analyzed how psychological factors associate with the progression of glucose control in 195 individuals from the All New Diabetics In Scania (ANDIS) cohort, who attended semiannual visits over four years for analysis of HbA1c and completion of questionnaires Fig. . Of thes15. This involved repeated testing by patients, including feedback on content, language, and relevance. The tool is composed of 80 themes on different areas  or wait for 12 weeks  in participants with access to the tool. In participants randomized to wait, average HbA1c decreased by 1.3 mmol/mol . The mean difference between the groups was −1.9 mmol/mol . When adjusting for potential confounders related to study discontinuation, the mean difference was −2.2 mmol/mol (95% CI −4.1 to −0.3).The average HbA1c decreased by 3.2 mmol/mol from baseline to twelve weeks . This suggests that the response to the tool was larger in participants with MOD compared to those without MOD.We investigated whether patients in the MOD cluster responded differently to the tool compared with patients in other clusters. Interestingly, participants with MOD characteristics , in order to be exposed to the different areas covered by the tool while providing sufficient time to implement changes between sessions. In the 274 participants included in the long-term assessment (Supplementary Table n = 59) demonstrated a 6.0 mmol/mol average reduction of HbA1c at the end of follow-up relative to baseline  when the tool was used at least monthly and 3.6 mmol/mol  when used at least bimonthly (every two months)  between MOD/non-MOD characteristics and users/controls. This shows that participants with MOD have a pronounced HbA1c response during long-term follow-up. Compared to non-MOD participants they had also larger improvement in insulin resistance  and ameliorated insulin secretion , while the average change of body weight was −1.5 kg .To analyze how the individual pathophysiology influenced the long-term response, we compared participants with or without MOD characteristics during follow-up. Participants with MOD  had a considerably larger HbA1c response, also when the tool was used less than recommended. The mean difference between the less-frequent users with MOD and matched controls with MOD was −6.4 mmol/mol (95% CI −11.0 to −1.8). When analyzed in a linear model, there was a significant interaction (n = 71), as assessed by repeated International Physical Activity Questionnaires.The reduction of HbA1c was accompanied by decreased body weight, fat mass, insulin resistance and fasting glucose, increased muscle mass, reduced systolic blood pressure and improved HDL cholesterol, without observed changes in total cholesterol, LDL cholesterol, triglycerides, or diastolic blood pressure Table . The chan = 204), and a 2.5 mmol/mol (95% CI −4.7 to −0.3) larger response in participants with BMI above median (30 kg/m2) compared to those below. Lower body weight and insulin resistance would be expected to ameliorate beta-cell stress and insulin secretory capacity20 .Accordingly, we observed an association between exposure to the tool and improved insulin secretion, measured by HOMA2-B .To better understand the pathophysiological changes in participants exposed to the tool, we examined to which extent reduced body weight and insulin resistance mediated the improved glycemic control. Reduced body weight was associated with improved HbA1c, and weight reduction was indicated to be a partial mediator of the total effect of using the tool (number of completed themes) on HbA1c improvement  or more concrete . A transformer-based language model was trained with questions from users of the tool who were not part of the evaluation study. The resulting model achieved a classification accuracy of 88.4% on the test set. Of the questions asked by study participants, 55.5% were classified as concrete and 44.5% as abstract. Interestingly, the number of abstract questions was associated with improved HbA1c, while there was no association between the number of concrete questions and change of HbA1c  questions to themselves had improved HbA1c. This observation is also in accordance with self-affirmation theory, which was the conceptual foundation of the study23.There was a positive correlation between exposure and improvement of HbA1c, which was accompanied by reduced body weight and insulin resistance, ameliorated insulin secretion and lower systolic blood pressure. The reduction of blood glucose is partially mediated by weight loss and improved insulin resistance. This is also compatible with the pathophysiological data in participants with MOD, who exhibited a pronounced glucose reduction even when using the tool less than recommended. There is considerable interest in personalizing treatment for metabolic diseases based on the pathophysiology, but practical feasibility has been unclear. The findings could have general implications by providing a means to identify those who are most likely to respond to lifestyle treatment based on the pathophysiology. As an option to cluster assignment, a BMI cut-off may also be used12. The study has also a number of limitations. No adjustment was made for multiple comparisons, and the variables should therefore be interpreted based on the overall pattern of outcomes. A fundamental problem to lifestyle studies is expectancy artefacts, as individuals randomized to wait may be affected in unpredictable ways, including changed motivation24. We therefore used two different control conditions. To better control for potential selection bias, study participants were first randomized to a wait list or to access the tool, with full allocation concealment. This was complemented by comparisons between study participants and matched controls in clinical registries, which increases the risk for selection bias but on the other hand reduces expectancy artefacts24.While previous meta-analyses have shown declining effect of lifestyle programs after six months, the metabolic improvement in response to the tool was sustained during two years, making this one of the longest and most extensive investigations of digital lifestyle treatment to dateStudy participants may be more motivated to lifestyle changes than patients in general because of potential selection bias. The exposure-response relationship at three-month time resolution showed that change of HbA1c during different quarterly periods even within the same individual is related to usage  than in the control group  during the randomization period, probably because using the tool requires an active engagement that may conflict with other activities or interests to a higher extent than being on a wait list. The development of the tool involved semi-structured interviews on patient needs and preferences as well as repeated usability testing and iterative improvements to facilitate usability and uptake. A limitation is that we did not include any patient-reported usability measures during the subsequent evaluation study. Such measures could have provided information on how to further improve the relevance of the tool and given more detailed data on reasons for discontinuation, in addition to the metabolic outcomes.It is of note that more participants discontinued in the intervention group  did not use the tool during the long-term follow-up. These rates are comparable with what has previously been observed in meta-analyses of digital tools27. We found that the tool is clinically valuable also when adherence is variable, suggesting broad clinical applicability. In view of the severe clinical need for scalable solutions to treat type 2 diabetes the findings offer a means for affordable lifestyle management that could benefit a large number of patients. During follow-up, average HbA1c was improved by approximately 6 mmol/mol both in the ~20% who used it biweekly and in the 30% of patients who have MOD characteristics, even at less-frequent usage. The HbA1c reduction is of similar magnitude to common glucose-lowering drugs, for example sodium glucose cotransporter type-2 (SGLT2)28 inhibitors or dipeptidyl peptidase-4 (DPP-4)29 inhibitors added to metformin or increased insulin treatment by 0.2–0.3 IU per kg30. The marginal cost is, however, fundamentally lower than pharmacological treatment and existing lifestyle intervention programs, and overall cost-effectiveness is consequently much less sensitive to variation in compliance. We demonstrate that even when not all patients use the tool as recommended it would still be cost-saving, in contrast to the cost-benefit model of most drugs and existing lifestyle support. A clinician could therefore provide the tool to all patients with type 2 diabetes, as any additional patient incurs a minimal cost, or direct it to patients with high body weight and insulin resistance, analogous to a tailored drug.Dropout rates could potentially have been reduced by enhanced efforts from study personnel to motivate participants. In order for digital lifestyle solutions to be scalable and broadly applicable, it is however essential that efficacy does not depend on in-person reinforcement or increased healthcare activitiesIn summary, the study presents a self-reflective approach to lifestyle treatment that has previously been lacking. Patients with type 2 diabetes regularly using the tool had improved metabolic control over two years, with a pronounced response in a pathophysiological subgroup of obese insulin-resistant individuals. The tool does not require additional healthcare resources, which is often a major limitation for broad utility. It therefore opens an avenue to address the large unmet need for scalable treatment of lifestyle diseases with sustained efficacy.We developed a lifestyle treatment on the theoretical foundation of self-affirmation and implemented it as a digital tool to enable broad applicability. The tool is web-based and is used as a stand-alone continuous support without requirement of additional healthcare activities. It is available in English and Swedish and is provided for free via academic institutions.We conducted a clinical study  to test the hypothesis that glucose control would improve in patients with type 2 diabetes using the digital tool. The study aimed to investigate the pattern of usage and metabolic outcomes under conditions that were as similar as possible to real-life settings . At study start, participants were randomized to access the tool or wait for twelve weeks (1:1 ratio). The two groups were then merged to enable all participants to use the tool during an extended open-label period for up to three years.Participants used the tool at their preferred pace but received written recommendations within the tool to complete a theme biweekly. Each exposure to the tool takes approximately 10–30 min and typically involves one completed theme. Study personnel were instructed to remain neutral at blood sampling visits and not reinforce usage in order to assess the frequency of use and resultant outcomes that can be expected in real-life situations over extended time without the need for increased healthcare support.1.The effect on HbA1c in participants using the tool was compared with both randomized controls and matched controls on usual care. Usual care refers to routine diabetes management and treatment based on the general guidelines from the European Association for the Study of Diabetes and the American Diabetes AssociationThe study was performed according to the Declaration of Helsinki and Good Clinical Practice. The protocol (Supplementary Data 1) were eligible for enrollment. Diabetes mellitus was diagnosed in routine healthcare based on the WHO criteria (fasting plasma glucose ≥7.0 mmol/l or 2-h post-load plasma glucose ≥11.1 mmol/l or HbA1C ≥48 mmol/mol). All participants provided written informed consent. Exclusion criteria were: type 1 diabetes, maturity-onset diabetes of the young (MODY), secondary diabetes, other conditions, treatments or participation in clinical studies that in the judgment of the investigator could affect the evaluation, or conflicts of interest .Patients older than 35 years with prior documentation of type 2 diabetes and HbA1c ≥52 mmol/mol , body weight, blood pressure and bioimpedance. Participants were also requested to report medication changes. Total follow-up time varied between participants, as they were not all recruited simultaneously.The study center was located at Scania University Hospital, Malmö, Sweden. Participants received travel reimbursement to enable individuals from all over the region (southern Sweden), also outside the main cities, to take part. No other financial incentives were provided. Study participants were managed by their ordinary healthcare providers throughout the study.26. Participants received regular emails to prompt continuous usage but no reinforcement by study personnel, who was instructed to remain neutral at visits, as we wanted to assess the natural usage pattern and resultant outcomes without external interference. Technical problems were referred to a study coordinator, who also responded to requests to clarify content in a general manner without providing personal advice.Healthcare providers and study personnel were not involved in the participants’ activities on the tool. The participants used it at their preferred pace but received written recommendations within the tool to complete a theme at least every other week. The recommended frequency of use intended to enable sufficient time to implement changes between sessions while maintaining awareness of the questions and being exposed to the different areas covered by the tool during a year. It was in line with the preferences expressed by patients in the test sessions and with previous recommendations for digital lifestyle toolsAfter the first visit, participants received an introductory email with a secure web link to a personal study account and instructions on how to get started. By clicking the link, they set a password, completed a questionnaire, and were randomized via a web-based system to have immediate access to the tool or wait for twelve weeks. In this manner, allocation was concealed to both participants and study personnel at the first visit .Participants randomized to wait received usual care via their ordinary healthcare provider. They were invited to a second visit after twelve weeks for blood sampling and physical examination. Following that visit, they received an email message with a link to complete the questionnaire once again and were then able to access the tool.The randomization sequence  was generated by external statisticians, who had no contact with participants or study personnel. It was uploaded by external technical staff to the web-based allocation system. Thus, the generation of the random sequence, participant enrollment by study personnel, and the web-based system for allocation to randomization groups were clearly separated.After the initial 12-week period, all study participants had access to the tool and were compared with matched controls on usual care. Controls were selected from patients with type 2 diabetes in the ANDIS registry. ANDIS was approved by the regional ethics review committee in Lund (584/2006 and 676/2012) and aims to register all incident cases of diabetes in Scania, which is one of the largest regions in Sweden with 1,200,000 inhabitants in both rural and urban areas and a wide distribution of socioeconomic background. Approximately 23,000 diabetic patients (>90% of the estimated number of eligible cases in the region) are included as of December 2020. Most individuals with type 2 diabetes are managed in primary care. Fasting blood glucose, C-peptide, glutamic acid decarboxylase (GAD) antibodies, length and body weight are measured at registration. Prospective data on metabolic variables and medication are obtained via the Swedish National Diabetes Registry and clinical registries on drug prescription and laboratory tests.The controls were matched exactly with study participants on gender and on Mahalanobis distance based on age (at the index date), body mass index (BMI) and HbA1c. The first possible index date for controls was set to be at least two years after the diagnosis date. The index date was selected at random among all registrations meeting the requirements for available follow-up time with no medication change during follow-up (as for the intervention group). Selection of the index date and potential controls were done in a Statistical Analysis System (SAS) program. Balance before and after matching was evaluated using the standardized mean difference (SMD).14. Four clusters of type 2 diabetes patients were highlighted, each with different pathophysiological characteristics . The findings have been replicated in additional cohorts31. The clusters do not represent separate disease entities32 but may be used as a practical means to distinguish individuals with different pathophysiology. Cluster assignment of individual patients can be done clinically via an online program that uses age at diagnosis, sex, BMI, HbA1c, fasting glucose and C-peptide as input variables14 .A data-driven cluster analysis of 9000 patients with diabetes has recently been performed in the ANDIS cohort based on six variables measured at diagnosis: GAD antibodies, age, BMI, HbA1c, and homeostasis model assessment-2 estimates of insulin resistance (HOMA2-IR) and beta-cell function (HOMA2-B)13. Moreover, it is currently unclear how the individual pathophysiology influences the response to lifestyle interventions in individuals with manifest type 2 diabetes. Since lifestyle changes have been shown to affect insulin resistance18 and lead to larger glycemic improvement in patients with relatively well-preserved insulin secretion19 , we hypothesized that the tool would be more effective in patients with MOD than in patients without MOD. Study participants and matched controls were therefore categorized as MOD and non-MOD, respectively, based on the published clustering methodology14. Comparisons were adjusted for differences in baseline HbA1c between MOD and non-MOD participants. The variation in BMI between MOD and non-MOD corresponds to the typical phenotypes, and adjusting for baseline BMI would therefore blur the clinical features that we aim to contrast.One of the clusters, termed Mild Obesity-related Diabetes , is characterized by high BMI and insulin resistance but relatively well-preserved insulin secretion. Studies on diabetes prevention have indicated that the response to lifestyle interventions may be larger in those with high BMI and insulin resistance, but data are conflictingThe following baseline data were collected at the first visit: age, sex, BMI, level of education, socioeconomic status, smoking habits, time since diabetes diagnosis, and current glucose-lowering treatment.The primary study variable was HbA1c in blood.Secondary variables included: body weight, blood pressure, fat mass and muscle mass as estimated by bioimpedance , fasting blood glucose, total plasma cholesterol, plasma low density lipoprotein (LDL) cholesterol, plasma high density lipoprotein (HDL) cholesterol, plasma triglycerides, and HOMA2-IR and HOMA2-B based on fasting glucose and C-peptide.Blood samples were taken in the morning (between 7.30 and 10.00). Participants were instructed to be fasted since 10 pm the previous day and to avoid nicotine use the same day and alcohol consumption and strenuous physical activity within 24 h of the visit. Fasting blood glucose was measured at the study center using a HemoCue Glucose System . All other blood analyses were performed at the central hospital laboratory . HbA1c was analyzed according to International Federation of Clinical Chemistry (IFCC) standard by a Capillary 3 TERA Hemoglobin A1c Kit. C-peptide, total cholesterol, LDL cholesterol, HDL cholesterol and triglycerides were measured on Cobas .33.HOMA2-IR and HOMA2-B were calculated based on C-peptide concentrations  using the HOMA calculator Blood pressure was measured using a standardized cuff adapted to the size of the participant’s arm after the participant had rested in a sitting position for at least ten minutes. Height was recorded in centimeters and body weight in kilograms, to one decimal place, with light clothing and no shoes.Bioimpedance measurements were done using a BIA101 to obtain values of resistance and reactance. Fat-free mass (FFM) was calculated using the Geneva single BIA equation  was estimated using the multi-component sex-specific model developed by Sun and colleagues (equations 35 .Fat mass (FM) was calculated by deducting the FFM from the total body weight of the individuals. Muscle mass (MM) was estimated based on the assumption that MM is the remaining mass of the FFM after deducting extracellular water (~29%) and bone mineral (~7%)36. Participants were instructed to complete the questionnaire on their own to avoid observer bias. EQ-5D-5L score ranges from 0 to 1 with higher score indicating better quality-of-life. EQ-5D-5L scores were not available from controls, and change of EQ-5D-5L score was therefore analyzed from baseline to one year in study participants using paired comparisons.Every three months, the participants completed a questionnaire with the European Quality of-Life (Euro-Qol) Five Dimension Five Level Scale (EQ-5D-5L), a preference-based measure of quality-of-life. The questionnaire was separate from the tool and completed by participants via their study account online using a quarterly reminder system. The responses from EQ-5D-5L were converted to a sum score using the official scoring system provided by Euro-Qol37 .This questionnaire was completed by users within the tool at less strict time intervals than EQ-5D-5L. IPAQ was therefore included only as an exploratory variable . Responses were converted to Metabolic Equivalent Task minutes (MET-minutes) per week according to the IPAQ scoring protocol37. MET-minute scores are equivalent to kilocalories for a 60-kg person, and the number of kilocalories were computed from MET-minutes asParticipants also completed the International Physical Activity Questionnaire (IPAQ), which assesses intense and moderate physical activity as well as walking during the last seven daysMET-minutes x (body weight in kilograms/60 kilograms).Supplementary Table A fundamental problem in lifestyle interventions is control comparisons. The expectancy artefacts that may occur when individuals are randomly assigned to the least preferred option, whether it is a wait list or strict continuation of usual care, are difficult to predict. It could be envisioned that assignment to a wait list (with promise of the tool after 12 weeks) for some individuals is more motivating than being assigned to mere usual care (never access to the tool within the study) and could inspire healthy behaviors already during the wait period, while others may of course react in opposite ways. We also reasoned that the risk for discontinuation is higher when participants are randomized to strict usual care compared with wait list controls.Based on this reasoning we used two different control conditions: wait list controls during a randomization period, with the inherent risks of expectancy artefacts, and an extended open-label period where all participants had access to the tool and were compared with matched controls on usual care, which increases the risk for confounding effects but on the other hand reduces expectancy artefacts. We performed a number of analyses to adjust for potential confounders related to the different control conditions.First, in the randomization phase, we used analysis weights based on propensity scores to adjust for potential confounders between participant randomized to wait and to access the tool as a result of participant drop-out during the randomization phase. The weights are based on the probability of continuing the study and are applied to statistically adjust the composition of the group of non-dropouts who accessed the tool, taking the baseline characteristics of the drop-outs into account.Second, in the randomization phase, a total of 99 of the 143 participants randomized to access the tool used it as recommended (at least biweekly) during the twelve weeks. To adjust for any systematic differences related to adherence, we applied analysis weights based on the probability of using the tool as recommended to statistically adjust the composition of the groups in order to estimate the mean difference if the groups were more comparable in terms of baseline characteristics.Third, in the subsequent long-term assessment period, we also used propensity scores to account for potential confounders related to frequency of using the tool. Specifically, we contrasted biweekly users with non-users in order to estimate the effects that would be observed if the frequent users had the same composition as non-users in terms of sex, age, BMI, diabetes treatment, socioeconomic status, education and disease duration.Analysis weights based on propensity scores is a state-of-the-art method to adjust for potential confounders, and the comparisons were made using a linear model with change in HbA1c as the dependent variable and participant group as the only independent variable. We used both unadjusted and adjusted analyses, where the adjustment is intended to correct for confounders related to adherence or dropout between participant groups. The analysis weights were defined as follows for the different analyses:First, the inverse of the probability of being a non-dropout in the treatment group and one in the wait group during randomization; second, the inverse of the probability of using the tool as recommended in the access group and one in the wait group during randomization; third, the inverse of the probability of being a user of the tool for the group actively using it and the inverse of one minus the probability of being a user of the tool for the group of non-users.The probabilities were estimated using gradient boosting method (GBM) with shrinkage 0.001, interaction depth 3 and the number of trees optimized to minimize the average standardized mean difference using cross validation. The weighed analyses used a robust sandwich estimator for the standard error to account for the weights. Missing data is handled by the GBM as the algorithm splints only on observed values. The analysis was done using R 4.0.2 and specifically the twang package for the estimation of the analysis weights.We did a quarterly exposure-response analysis to assess whether change of HbA1c over time within the same individual is directly related to whether the tool is actively used or not during different quarters. This indicates whether the observed effects are consequences of baseline traits and higher initial motivation in some participants or coupled with exposure to the tool.Since the exposure-response analysis does not fully exclude the possibility that frequency of use could be a marker for fluctuating motivation during the follow-up period, we also performed a semantic analysis of user behavior on the tool (see below). Specifically, we examined whether the glycemic response differed based on the number of concrete and abstract questions, respectively, asked by participants. This analysis aimed to better understand whether the metabolic improvement is a result of exposure to specific elements of the tool.To further address to what extent the observed effects may reflect spontaneous fluctuations in glucose control, we analyzed patterns of HbA1c progression in the ANDIS cohort and compared these data with HbA1c progression in users of the tool (see below).In addition to potential confounders captured by the measured baseline variables, differences in other baseline traits or initial motivation may also confound the results. It is very difficult to completely rule out all possible confounders in lifestyle studies, but we performed a range of analyses to address this matter as thoroughly as possible.Participants attended quarterly visits during the first year. We examined the number of completed themes and the corresponding change of HbA1c relative to previous visit for each quarterly period in all participants. The average change of HbA1c during quarterly periods with none completed themes, with 1–4 completed themes and with at least 5 completed themes across all participants was determined.We analyzed trajectories of HbA1c progression in 13,561 patients diagnosed with type 2 diabetes between 2008 to 2018 in ANDIS with prospective HbA1c data measured in routine healthcare. A total of 4,602 patients had at least three HbA1c measurements over a three-year time frame at an average of 1,320 days (3.6 years) from diagnosis. Patients in ANDIS who were also included in the evaluation study of the tool were removed.Sustained decrease relative to baseline: at least one HbA1c measurements was lower than baseline HbA1c at the start of the three-year time frame and all other measurements were lower than or equal to baseline.Sustained increase relative to baseline: at least one HbA1c measurements was higher than baseline HbA1c and all other measurements were higher than or equal to baseline.Oscillatory, predominantly decreasing: the majority, but not all, of the measurements were lower or equal to baseline HbA1c.Oscillatory, predominantly increasing: half of more, but not all, of the measurements were higher than baseline HbA1c.We identified four common patterns of HbA1c trajectories:A total of 1358 patients had a baseline HbA1c of at least 52 mmol/mol and no known change of glucose-lowering medication during the three-year time frame . The average follow-up time between first and last HbA1c measurement for these patients was 632 days (as compared with 730 days for users of the tool).We analyzed HbA1c progression in users during the follow-up period of the tool in a similar manner.The number of patients corresponding to each pattern was compared between ANDIS patients and users of the tool using Fisher’s exact test.To better understand the effective elements of the tool, we investigated, as an explorative analysis, whether specific behavior change techniques (BCT) were overrepresented in themes completed by users who responded to the tool by improved HbA1c from baseline to one year. These analyses focused on the first year of the study, during which participants attended quarterly visits.For every quarterly period , we examined which participants had completed a particular theme. We further analyzed how many of those participants had lower HbA1c at the end of the period than at the start of the period (“responders”) and how many had similar or higher HbA1c (“non-responders”). Next, the 2–4 BCT that were included in the theme were weighted according to their dominance  or more concrete.38 and is trained to fill in  blanks in natural language text. The resulting “pre-trained” model can then be re-trained (“fine-tuned”) on new tasks using only limited training data and small model modifications38.The semantic analysis was performed using a machine-learning technique termed BERT . BERT is a transformer-based language model trained with a cloze-task objective39. KB-BERT is pre-trained on Swedish text from the 1940’s and onward, consisting of digitized newspapers, official reports of the Swedish government, legal deposits to the National Library, various social media data, and Swedish Wikipedia. KB-BERT is the most performant Swedish BERT model, outperforming multilingual BERT and other Swedish BERT models on a range of fine-tuning tasks39.In this study, we used a BERT model via the National Library of Sweden, called KB-BERT (Kungliga Biblioteket-BERT)We used Huggingface transformers to retrieve the pre-trained KB-BERT model and trained it on classifying questions as either concrete or abstract using standard procedures for single-document classification. i.e. the classification output representation is fed to a single layer softmax classifier, training “all” parameters of the model.KB-BERT was trained with questions from users of the tool, including test users, who were not part of the evaluation study. The total number of questions was ~5000, and the number of unique questions was ~1900. For evaluation purposes, we constructed a 50-50 train-test split over all unique questions  Thus, we trained the abstract-concrete classifier on half of the unique questions, and evaluated the resulting model on the remaining half. We trained the model for one epoch using an AdamW optimizer with a constant learning rate of 1E-5.The resulting model achieved a classification accuracy of 88.4 % on the test set. Of the questions asked by study participants, 55.5% were classified as concrete and 44.5% as abstract, with a few examples as below:How limited am I and what opportunities do I have to influence my health?Am I prioritizing what is really meaningful?Could my family also benefit from me changing breakfast habits?How can I see moderation as something valuable rather than a sacrifice?Abstract questions:What can I do to lose 5 kg of body weight?How can I reprioritize my daily life to get more time for exercise?What could I change to reduce the glycemic index of my breakfast?How could I eat less?Concrete questions:Before constructing the digital tool, we analyzed how psychological factors of importance to diabetes management associate with the progression of glucose control in individuals with type 2 diabetes. A total of 2000 individuals in the All New Diabetics In Scania (ANDIS) registry in Sweden were invited via letters, of which 195 accepted to attend semiannual visits over an average of 32 months  to 10 addresses to what extent reflections on overall life context are influencing health-related habits.Perceived influence of life view on health-related habits with a 10-point Likert scaleAt each visit , the level of glycated hemoglobin (HbA1c) in blood was analyzed. The patients also completed a questionnaire including the following scales:The association between each score and HbA1c across visits was analyzed using a linear model in which values from each visit were included as discrete observations and grouped by study subject.The unstandardized beta coefficients from the linear models are reported with 95% confidence intervals in Supplementary Table Of these participants, 30 were also invited via letters to take part in interviews to examine needs, attitudes and barriers to diabetes self-management . We judged this number to be appropriate to meet the aims of the interviews, be practically manageable and in line with principles for good qualitative methodology. The interviewees were selected based on maximum variation sampling to ensure a diverse sample based on age, disease duration, treatment and HbA1c. The interviews were conducted by a psychologist with extensive knowledge of diabetes and diabetes research, who had no prior relationship with the participants.The interviews were semi-structured and covered the experience of having diabetes, barriers to and previous experience of making lifestyle changes, perceived competence for diabetes management, as well as current self-management support and goals.43. In brief, all interviews were audio recorded, transcribed verbatim and checked against audio recordings for accuracy. The qualitative data analysis software program ATLAS.ti (version 8.2.4) was used to organize the analysis process.A full description of the interviews is presented elsewhereA total of 22 individuals accepted taking part in the interviews and provided written informed consent  can be managed more effectively by reflecting on strengths in other domains. From that broader perspective people may regard changes to specific behaviors as less threatening to overall self-integrity, leading to a less defensive attitude. Self-affirmation is associated with both greater intentions for behavioral change and actual behavioral change46.Self-affirmation theory is based on the observation that people may react defensively when reminded of unhealthy behaviors and therefore reject information that threatens self-integrity, i.e. the concept of oneself as acting in line with existing norms and personal values and beliefs47. We incorporated the principles of MI in the tool to promote reflection on ambivalence and commitment to change, which has been shown to increase intrinsic motivation48. To enable this without a traditional interviewer, the digital tool contains a large number of questions to stimulate self-reflection. Self-reflection may support autonomy16 and has played an important role in, for example, later forms of cognitive behavioral therapy49. It has, however, not been of major focus in diabetes self-management.Motivational interviewing (MI) has previously been demonstrated to affect dietary behavior and weight reductionThe tool was developed in an iterative manner with repeated testing by patients, including feedback on usability and content. Patients who had participated in the interviews were exposed to computer-based assignments focusing on self-reflection and on viewing current lifestyle in a larger context, in line with self-affirmation theory and MI . Some participants found these assignments difficult and too personal, while the majority considered them helpful in order to think in new ways about type 2 diabetes and reflect on current priorities. Participants highlighted that the focus on self-reflection, which was regarded as rather unusual in diabetes care, could increase the sense of personal relevance and adherence to health information.The tool was also demonstrated to physicians, nurses, dietitians and psychologists with extensive experience of meeting patients with lifestyle-related diseases. Patients and healthcare professionals tested early versions of the tool at individual sessions and commented on usability, language, design, perceived purpose, relevance and opportunities for improvement.After each session they were asked to respond to the following questions: Would you personally have use for the activity you just did? How was it to do the activity? Can you provide three positive and three negative points from your experience? Is there something you think needs to be improved? What would you say was the purpose with this activity? Do you think the activity is of relevance to your diabetes disease? Was something in language or design unclear?The testing and iterative modifications were conducted by psychologists and physicians in the study team and by external technical experts.Based on the feedback and observations of user behavior, a complete digital tool was developed . The tool is web-based with a responsive design to enable patients to use it via their own computer, tablet or mobile phone. All data are transferred via secure protocols and stored on secure servers.5. We therefore based the themes on the comprehensive theoretical framework and taxonomy proposed by Michie and colleagues50, which has been widely accepted5. Each theme of the tool incorporates 2–4 BCT . Supplementary Table The tool is composed of 80 different themes, focusing on diet and exercise but also a range of other areas, including stress management, decision-making, social interactions, loneliness, and the emotional burden of having diabetes. Previous systematic reviews have highlighted the importance of including defined behavior change techniques (BCT) to better understand the effective elements of lifestyle interventions and generalize findingsA self-assessment test with automated feedback, aiming to raise awareness of current behavior.3. Other exercises may involve time prioritization, mindful eating, and methods to cope with distress and automatic thoughts.An exercise such as evaluating short- and long-term benefits and losses associated with health-related goals, in order to identify willingness and barriers to change51. All texts were written by scientists specifically for this tool, underwent careful editing for style, tone and language by a panel of physicians, journalists and authors, followed by final approval for accuracy and clarity by the principal investigator.Informative texts (approximately 500–800 words per text) on topics of relevance to health and lifestyle-related diseases. Texts are based on international consensus recommendations for lifestyle managementA theme takes approximately 10–30 min to complete and contains one or more of the following components:48 and the needs expressed in patient interviews. It aims to facilitate sustainable lifestyle changes by raising general awareness of current priorities and helping patients explore different options and choose their own course of action.The tool is accessed via any web browser using a study ID and a password. At each login, users select a theme they consider to be of relevance. At the end of the theme, they ask a question to themselves to promote reflection on how the information and insights from the theme could be implemented in daily life. This methodology is in line with self-affirmation theory and MIHow can I reprioritize to get more time to cook healthy food?How can I stop blaming myself and focus on my good habits?How can I restructure my home environment to reduce the signals that make me eat so often?In every theme, users can choose from three to five example questions or write a question on their own. Questions typically take the form “How can I…”, for example:Who could motivate me to change my lifestyle?Have I really taken in that diabetes is a serious disease and am I doing what I can to manage it?Am I eating now because I am hungry or because of old habits?What is the smallest thing I can do to get more physical activity into my everyday life?They may also take a freer form, for example:After completing a theme and choosing a personal question, the users log out; they are encouraged to ponder on these questions in their daily lives and return to the tool within two weeks. Users are then given the opportunity to comment briefly in a diary on whether the question led to any behavioral changes. They select a new theme or revisit one they have previously found valuable. Users can refine earlier questions or ask new ones, in order to stimulate an evolution of questions as they proceed through the tool.53. These themes aim to stimulate questions on overall life context and how it relates to current habits and disease coping  has proposedThe tool is supposed to be used as a continuous support without a finite number of sessions . It is technically prepared for multiple languages with a language select function and is currently available in English and Swedish. The tool is constructed to enable large scalability and can be implemented as a stand-alone support or combined with other lifestyle management activities. Providing the service to 50,000 users  requires a part-time coordinator  and technical personnel (50% full-time employee for updates and maintenance) in addition to server costs.To maintain privacy, healthcare providers are not able to access user data. Technical functions are, however, prepared to enable users to download a report of their personal questions and reflections in case they wish to share all or parts of their activities on the tool with healthcare professionals to facilitate consultation .54. The analysis was based on assessment of risk factor control, which is a treatment goals because it has an impact on long-term risk of complications. Model simulations based on risk factors are often used in diabetes interventions, rather than within-trial analyses, as the time horizon of most trials is too short to cover expected benefits.We used a validated model, the Institute for Health Economics (IHE) Diabetes Cohort Model, to evaluate the cost-effectiveness of the tool and conducted multiple sensitivity analyses54. It is constructed with Markov health states representing important microvascular complications  and macrovascular complications . The model is flexible with several input parameters, including baseline characteristics of the cohort, choice of risk equations, treatment sequences, unit costs and quality-adjusted life-year (QALY) weights. Model results (costs and QALYs) are discounted using a 3% annual discount rate.The IHE model uses cohort data to estimate the cost-effectiveness of treatment for diabetes56 or Swedish clinical registries59 for patients with similar age, disease duration and glycemic control, in order to obtain as precise measures as possible. The UKPDS study has established evidence for the importance of risk factor control in type 2 diabetes and is still considered some of the best-available data on HbA1c progression for standard of care treatment56 .The baseline characteristics used in the model  was therefore expected, in agreement with previous reports61. This drift was assumed to start after three years in study participants, as the HbA1c reduction was maintained, and even enhanced, beyond one year in this group. In control patients on usual care, the drift was assumed to start after one year, in accordance with the observed changes in those individuals.Mean HbA1c increases over time in patients with type 2 diabetesWe also analyzed cost-effectiveness for when the tool is used at least monthly or at least bimonthly, and also assessed the sensitivity to changes in key parameters and assumptions .60 and adjusted to 2020 price levels using the monthly consumer price index for healthcare from Statistics Sweden. Costs were converted from SEK to USD using the 2020 average exchange rate (9.3 SEK per USD) at the Riksbank of Sweden.Healthcare costs related to diabetic complications were based on a recent reportAnalyses of treatment costs were based on information from the study participants on drug usage, including daily dose (Supplementary Table The yearly expenses related to the tool consist of server cost ($0.13 per user), email expenses ($0.23 per user), as well as administration and technical personnel for user requests, continuous updates and improvements to the tool ($7.12 per user). These numbers are based on current actual costs for providing the service to 50,000 users . The costs for developing the tool was not included in the analysis, as it is already in place and ready to use.62, as well as monthly salary data from Statistics Sweden. The yearly production value was calculated as the average monthly salary, multiplied by 1.4 to account for social expenses, and amounted to $63,514 per person. This was applied in the model for individuals up to 65 years of age.Costs related to productivity loss were based on a recent study on absence from work because of diabetic complications60.Utility weights related to the health states in the model were in accordance with previously reported data60.Utility weights related to the health states in the model were in accordance with previously reported data30 and weighted in proportion to the number of patients in the cohort using insulin.The estimated yearly event rates for mild and severe hypoglycemia were 2.6 and 0.02, respectively, per patient using insulin, and 2.0 and 0.005, respectively, per insulin-naïve patient. These data were based on a recent studyThe two primary endpoints were:The change of HbA1c from first to second visit compared between participants randomized to wait and randomized to access the tool.t-test. The sample size, 142 participants in each of the randomization groups, was calculated to ensure at least 80% power at alpha = 0.05 to detect a significant difference between the groups, assuming that the true treatment effect of the tool is 2 mmol/mol over 12 weeks with a standard deviation of 6 mmol/mol for the change of HbA1c. (The standard deviation for the change of HbA1c during 12 weeks was estimated from patients in the ANDIS cohort with baseline HbA1c ≥ 52 mmol/mol.)The standard interval between first and second visit was 12 weeks. All participants were included in the analysis, independent of medication change or frequency of using the tool, except those lost to follow-up between first and second visit, from whom no data could be obtained. The change of HbA1c was compared between randomization groups using a two-sided independent The change of HbA1c from baseline to end of follow-up in participants using the tool as recommended compared to matched controls.t-test. We needed 24 participants using the tool as recommended and 48 matched controls to have 80% power at alpha = 0.05 to detect a significant difference between the groups, assuming that the true treatment effect of the tool is 5 mmol/mol with a standard deviation of 7 mmol/mol for the change of HbA1c. (The standard deviation for the change of HbA1c per year was estimated from observations of patients in ANDIS with baseline HbA1c ≥ 52 mmol/mol).Baseline for study participants was defined as HbA1c before getting access to the tool, which was the first visit for those randomized to immediate access and the second visit for those who were initially randomized to wait. If participants were lost to follow-up or changed glucose-lowering medicines during the observation period, then data from the last visit with unchanged medicines was used for analysis . No imputation was done. Recommended usage refers to biweekly usage or more during at least a one-year time frame in the observation period . Only matched controls with no registered changes to glucose-lowering medication during the observation period were used. The change of HbA1c was compared between study participants and matched controls (1:2 ratio) by a two-sided independent t-tests and are presented as averages with 95% confidence intervals.Secondary endpoints included the change of secondary variables from baseline to end of follow-up between study participants using the tool as recommended and controls. For controls, data on body weight, blood pressure, total cholesterol, HDL cholesterol, LDL cholesterol, and triglyceride levels during corresponding time from index date were obtained via ANDIS and the Swedish National Diabetes Registry, and data on fasting glucose, HOMA2-IR, HOMA2-B, fat mass and muscle mass were obtained from longitudinal measurements of ANDIS patients at the Clinical Research Center, Malmö, Sweden. Corresponding analyses were conducted for patients with less-frequent usage. The differences between study participants and controls were compared using independent The association between the number of themes completed and changes of HbA1c, body weight, HOMA2-IR and HOMA2-B, respectively, was analyzed using linear regression.Change of IPAQ score between initial and final test was analyzed in study participants by paired comparisons and presented as a point estimate with 95% confidence interval.The distribution of responder and non-responder sum scores for each BCT was analyzed by a chi-square test.The association between the number of abstract and concrete questions, respectively, and change of HbA1c from baseline was analyzed using linear regression and presented as beta coefficients with 95% confidence intervals.63 v4.0 in SPSS v26. This analysis was undertaken with the hypotheses that change of body weight as well as change of insulin resistance partially mediate the relationship between usage of the tool (measured as number of completed themes) and HbA1c improvement. The analysis gives estimates and confidence intervals of the indirect effect on HbA1c improvement mediated via change of body weight or change of insulin resistance. BMI at baseline and sex were used as moderators in the analysis of body weight, and baseline HOMA2-IR and sex were used as moderators in the analysis of insulin resistance. Data are decomposed into (1) effect of the independent variable on the outcome variable, (2) effect of the independent and mediator variable, respectively, on outcome, and (3) the total effect of the independent variable on the outcome, the indirect effect via the mediator and the fraction of the total effect that is estimated to be an indirect effect.Mediation analysis was performed using Hayes’ Process macroSummary statistics are generally presented as point estimates with 95% CI. The widths of the intervals have not been adjusted for multiplicity. Statistical analyses were performed using SPSS (v25 and v26).Further information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary material revised cleanSupplementary Data 1Reporting Summary


Respiratory distress syndrome (RDS) is one of the leading causes of neonatal death in the neonatal intensive care unit (NICU). Previous studies have suggested that the development of neonatal RDS may be associated with inflammation and lead to organ dysfunction. The neonatal sequential organ failure assessment (nSOFA) scoring system is an operational definition of organ dysfunction, but whether it can be used to predict mortality in neonates RDS is unknown. The aim of this study was to clarify the performance of the nSOFA score in predicting mortality in patients with neonatal RDS, with the aim of broadening the clinical application of the nSOFA score.Neonates with RDS were identified from the Medical Information Mart for Intensive Care (MIMIC)-III database. Cox proportional hazards model were used to assess the association between nSOFA score and mortality. Propensity score matched analysis were used to assess the robustness of the analytical results.p < 0.001]. Similarly, the High nSOFA group was significantly associated with higher mortality in RDS patients  compared with the low nSOFA group.In this study of 1,281 patients with RDS of which 57.2% were male, death occurred in 40 cases (3.1%). Patients with high nSOFA scores had a higher mortality rate of 10.7% compared with low nSOFA scores at 0.3%. After adjusting for confounding, multivariate Cox proportional risk analysis showed that an increase in nSOFA score was significantly associated with increased mortality in patients with RDS [adjusted Hazards Ratio (aHR): 1.48, 95% Confidence Interval (CI): 1.32–1.67; The nSOFA score was positively associated with the risk of mortality in cases of neonatal RDS in the NICU, where its use may help clinicians to quickly and accurately identify high risk neonates and implement more aggressive intervention. Respiratory distress syndrome (RDS) is a clinical syndrome characterized by respiratory distress and progressive aggravation in affected neonates soon after birth and is one of the most common clinical conditions dealt with in the neonatal intensive care unit (NICU) . It is ePrevious studies have suggested that the pathogenesis of neonatal RDS is more complex than commonly recognized and inflammation may be involved . NupponeThe neonatal sequential organ failure assessment (nSOFA) scoring system is an operational definition of organ dysfunction, which is widely used to identify the presence of life-threatening organ dysfunction among preterm infants with inflammation-related diseases such as infection and sepsis , 10.This study was developed to clarify whether nSOFA score could be used to predict mortality in patients with neonatal RDS. In order to broaden the clinical application of the nSOFA score and to find a new method to assist clinicians to quickly and accurately identify high-risk neonates that require more aggressive intervention.This is a retrospective cohort study with data from the Marketplace for Medical Information in Intensive Care III (MIMIC-III) database, a longitudinal single-center database that contains information related to patients admitted to the intensive care unit at Beth Israel Deaconess Medical Center  between 2001 and 2012 . The dat2) exceeding 25% for a minimum of 24 h or according to the international statistical classification of diseases and related health problem ninth revision (ICD-9) codes including code 769. For patients who were admitted to the NICU more than once, only the first NICU stay was included for analysis.The MIMIC-III database contains a total of 7,870 patients admitted to the NICU and patients who didn’t develop RDS were excluded. A total of 1,281 patients with RDS were included in the final study cohort, grouped according to nSOFA score level and survival status, respectively. The flow chart of patient screening is shown in 2 to achieve the peripheral oxygen saturation (SpO2), the platelet count and any requirement for glucocorticoid, inotropic, or vasoactive drugs. The SpO2/FiO2 were converted using the partial pressure of arterial oxygen (PaO2)/FiO2 ratio conversion where SpO2/FiO2 = 64 + 0.84 × (PaO2/FiO2)  . MissingO2/FiO2) , 14 and The primary outcome of this study was classified as all-cause mortality. Inpatient mortality information was obtained from the Hospital Information System, and mortality information for discharged patients was obtained from the US Social Security Death Index.The nSOFA score uses categorical scores with a total score range from 0 as best to 15 as worst to objectively describe (1) receipt of mechanical ventilation and oxygen to maintain a physiologic peripheral saturation which was scored 0 to 8; (2) inotropic or vasoactive drug support, including the use of corticosteroids for presumed adrenal insufficiency or catecholamine-resistant shock scored 0 to 4; and (3) the presence and severity of thrombocytopenia based on the most recent platelet measure scored 0 to 3  Table 1Table 1.t-test or the Mann-Whitney U test for continuous variables and χ2-test or Fisher’s exact test for categorical variables.Values are presented as the means ± standard deviations or medians as interquartile ranges (IQRs) for continuous variables and categorical variables are presented as total numbers and percentages. Comparisons between groups were made using the Student’s The area under the receiver operating characteristic curve (AUROC) was calculated to assess the accuracy of the nSOFA score and its subscales in predicting the primary outcome in patients with RDS. The cut-off values of the receiver operating characteristic (ROC) curve were identified by calculating the Youden index, which divided the high and low nSOFA groups. The Kaplan-Meier survival analysis were performed to evaluate the incidence rate of primary outcome events among groups according to various levels of the nSOFA score and discrepancies among groups were evaluated by log-rank test. Cox proportional hazards models were used to estimate the relationships between nSOFA score (per 1 score)/nSOFA groups (high nSOFA groups and low nSOFA groups) and primary outcomes. Characteristic variables with significant baseline differences or clinical significance were used as candidate predictors in the multivariate Cox regression model. Variables with more than 20% missing data were excluded from the analysis. The details of missing data are summarized in The propensity score matching (PSM) was used to adjust for ethnicity, sex, and gestational age to ensure the comparability across of high and low nSOFA groups and between survival and non-survival groups in the analysis of baseline characteristics. Baseline characteristics of the original and matched cohorts were presented separately. Multivariate Cox regression analysis was also performed on the matching cohort. Subgroup analyses was used to investigate whether the risk was modified by sex and septicemia. All analyses were first unadjusted as model one, then adjusted for sex, ethnicity, gestational age, weight, and heart rate to get model two and finally for anemia, bleed, septicemia, and pulmonary surfactant to get model three.p-values < 0.05 for all analyses were considered statistically significant.All data analyses were performed using R software  and SPSS statistical software (SPSS Statistics 24.0). Bilateral There were 1,281 patients with RDS who were analyzed between 2001 and 2012 of whom 733 (57.2%) were male and 1,127 (93.1%) were less than 37 weeks of gestational age. There were 40 (3.1%) patient deaths in this cohort. The optimal cut-off value for nSOFA score associated with death was defined as 2.5 and this was used to divide the cohort into low and high nSOFA groups.2/FiO2, less urine output, additional complications such as acute kidney injury, bleeding and infection and had higher rates of ventilator and vasoactive drug use on the first day of admission . Baseline information for the original cohort and matched cohort of RDS patients with high and low nSOFA scores is shown in There was a total of 347 (27.1%) patients with a high nSOFA score, of whom 37 (10.7%) died, compared with 3 (0.3%) of the 934 (72.9%) patients with a low nSOFA score. Patients in the high nSOFA group had more gestational age less than 28 weeks, smaller height, weight and head circumference, lower SpOp < 0.001) and subscales . The mortality rate of RDS patients increased with increasing nSOFA score . The area under curve (AUC) for the nSOFA score predicting death in patients with RDS was 0.89 , which was higher than the predictive efficacy of the subscale  (p < 0.001), which was higher than that of the original nSOFA score and gestational age alone [0.73 ].Compared with survivors, non-survivors had higher nSOFA scores ] . The opt 0.690)] . We addep < 0.001). Univariate Cox proportional risk analysis showed that each unit increase in nSOFA scores was significantly associated with mortality . Even after adjusting for sex, ethnicity, gestational age, weight, heart rate, anemia, bleed, septicemia, and pulmonary surfactant, each unit score increase in nSOFA score remained significantly associated with mortality in patients with RDS . Similarly, the high nSOFA group was significantly associated with higher mortality in RDS patients compared with the low nSOFA group , liver dysfunction, and renal dysfunction, but these systems are more difficult to measure in neonates . The nSO actions , whereas actions . In the  actions , so the This study filled a gap in this field and broadens the clinical application of the nSOFA score, by determining the predictive power of the nSOFA score for mortality in RDS patients and its use to identify RDS patients at increased risk of adverse outcomes. As an objective measure of clinical course, nSOFA score has a potential utility both as a proxy variable for disease severity and as a risk assessment indicator for short- and long-term clinical endpoints . In this2/FiO2 was converted and replaced by using PaO2/FiO2. All the above factors may have influenced us to draw more definitive conclusions. Finally, this study did not examine the non-ICU population, so its applicability to neonates with suspected RDS in the rest of the hospital on the wards or in the Emergency Department could not be determined by this study. In the future, more large-scale, multicenter studies on neonatal mortality should be conducted to further verify these results.This study had several limitations. The data were only extracted from MIMIC-III data, which was a single-center retrospective study and it was difficult to control for a variety of confounders and bias in the analyses. In addition, individual patient-level data on treatment variables such as fluids, time to pulmonary surfactant and time to vasopressors was not available. Maternal medication data during pregnancy and delivery were not available. The SpOThis study indicated that nSOFA score was associated with the risk of mortality in the neonatal RDS of NICU. The active use of the nSOFA score may help clinicians to quickly and accurately identify high risk neonates and implement more aggressive interventions.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Massachusetts Institute of Technology  and Beth Israel Deaconess Medical Center . Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.YL conceptualized the research aims, planned the analyses, and guided the literature review. SS extracted the data from the MIMIC-III database. SS and JG participated in processing the data and performing the statistical analysis. LL, JT, JX, and WC wrote the first draft of the manuscript. SS, JG, MF, QL, KC, and YL provided comments and approved the final manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.