Patient-reported outcome measures in type 1 diabetes outpatient care

RESEARCH ARTICLE

Hippokratia 2024, 28(1): 17-21

Rochate J1, do Vale S1,2
1Endocrinology Department, Faculdade de Medicina, Universidade de Lisboa
2Endocrinology Diabetes and Metabolism Department, Unidade Local de Saúde de Santa Maria
Lisbon, Portugal

Abstract

Background: Patient-reported outcome measures (PROMs) assess how individuals perceive the disease and its impact on quality of life, representing an important complement to the metabolic evaluation in type 1 diabetes mellitus (T1DM). This study aimed to assess the PROMs and their association with metabolic control.

Methods: A cross-sectional study of adults with T1DM was conducted in the outpatient Endocrinology department between October 2022 and May 2023. Clinical, demographic, and continuous glucose monitoring (CGM) data were collected. Three PROMs were applied: the diabetes psychological adjustment scale (ATT18), the World Health Organization well-being index (WHO-5), and the patient health questionnaire (PHQ-9). Descriptive and bivariate statistical analyses were performed.

Results: We included 56 participants, aged 41.2 ± 14.6 years, 58 % female, and 64 % of medium-high socioeconomic class. The disease duration of the cohort was 21 ± 14.6 years, with 44.6 % on continuous subcutaneous insulin infusion (CSII) and 39.3 % presenting microvascular complications. Glycated hemoglobin of the cohort was 8.0 ± 1.4 %, time in range (TIR) 52 ± 22 %, coefficient of variation (CV) 37 ± 8 %, and median time below range (TBR) 2 %. Individuals on CSII had higher TIR (p =0.03). CV was related to TBR (ρ =0.643, p <0.001). The majority had satisfactory psychological adjustment to diabetes (ATT18 ≥60), which correlated directly with WHO-5 (r =0.511, p <0.001) and inversely with depression symptoms (r =-0.676, p <0.001). No relationships were identified between metabolic control and PROMs (p =0.63).

Conclusions: Including PROMs alongside detailed metabolic evaluation allows for individualized decision-making and active patient participation in diabetes management. These results underscore the importance of preventing depression, promoting well-being, and enhancing diabetes psychological adjustment in these patients, aiming to improve their quality of life. HIPPOKRATIA 2024, 28 (1):17-21. 

Keywords: Type 1 diabetes, quality of life, metabolic control, patient reported outcome measures, psychological adjustment to diabetes

Corresponding author: Juliana Rochate, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal, e-mail: julianarochate@edu.ulisboa.pt

Introduction

Type 1 Diabetes Mellitus (T1DM) is a chronic, autoimmune disease often diagnosed at young ages with necessary lifelong insulin therapy and additional care regarding diet and lifestyle, resulting in a significant impact on daily life1,2. The patient’s elevated responsibility for self-management and the requirement for continuous and daily treatment can lead to psychological distress and reduced quality of life3,4. Therefore, T1DM control and treatment should extend beyond assessing and optimizing glycemic control and complications. It should also encompass evaluating the psychological dimension by analyzing satisfaction with healthcare, well-being, and quality of life through assessing patient-reported outcome measures (PROMs)5.

PROMs are direct reports that extract health outcomes from the patient. Unlike clinical efficacy measures, evaluate how the patient feels about their health condition and/or treatment, their expectations, and the impact on quality of life allowing a patient-centered approach by promoting communication and self-efficacy and involving the patient in setting goals for disease control6,7. Furthermore, PROMs can be quality indicators of the health system, enabling active patient participation to improve health care quality and guiding professionals to tailor their approach based on patient preferences, resulting in health gains7,8. Several studies have demonstrated that quality of life and psychological well-being determine the capacity and effectiveness of diabetes control as evidenced by the association between PROMs and glycemic control indicators, such as glycated hemoglobin (HbA1c)1,5,9,10. In the Portuguese context, Góis et al3 with the diabetes psychological adjustment scale (ATT18) adapted and validated for the Portuguese population, revealed a positive association between psychological adjustment to diabetes and HbA1c in both type of patients with T1DM and T2DM3,4 and suggested that lower distress and less depressive symptoms are associated with better psychological adjustment4. In young adults with T1DM, a positive association between psychological adjustment to diabetes and treatment adherence was suggested11. The objective of this study is to assess the PROMs and how well-being and depressive symptoms relate to psychological adjustment to diabetes, and evaluate the relationship between PROMs and metabolic control. 

Materials and Methods

Study Population

This study used a convenience sample of adults with T1DM seen in the Diabetes outpatient department of Endocrinology, Diabetes, and Metabolism of Santa Maria Hospital, Lisbon, between October 2022 and May 2023. Written consent was obtained from all patients, and the study was conducted following approval from the Ethics Committee of the Academic Medical Center of Lisbon-CAML (decision No 187/22, date: 15/07/2022). We obtained demographic and clinical information for enrolled patients. Glycemic control indicators were recorded, including: i) HbA1c from the latest analyses, ii) data from the ambulatory glucose profile generated by the FreeStyle Libre® software, obtained through the continuous glucose monitoring (CGM) system over the 14 days preceding the consultation day, and iii) presence of chronic complications. HbA1c was determined by high-pressure liquid chromatography, with a normal range of 4.0-6.0 % and a target <7.0 %, which is recommended for most adults diagnosed with T1DM12. Data obtained through CGM included: a) time in range (TIR): percentage of time in recommended glycemic range for individuals with T1DM (70-180 mg/dl), with a target value of ≥70 %; b) total time below range (TBR): percentage of time in hypoglycemia (<70 mg/dl), with the target value <4 % of the time; c) coefficient of variation (CV): glucose variability, with target values <36 % indicating greater glycemic stability12,13.

PROMs

Well-being was assessed using the World Health Organization well-being index (WHO-5), with a score ranging from 0 (absence of well-being) to 100 (maximum well-being), and suboptimal well-being was considered to score <509,14. The patient health questionnaire (PHQ-9) was used to evaluate the frequency of depression symptoms, with a score range of 0 to 25, and depression screening was conducted with scores ≥10 points15,16. We used the WHO-5 and PHQ-9 following the recommendations of the International Consortium for Health Outcomes7. To evaluate subjective adaptation to diabetes, the ATT18 was employed, comprising 18 questions related to stress and feelings of disintegration, guilt, embarrassment, and self-perceived ability to manage diabetes, including satisfaction with healthcare professionals3. Better psychological adjustment was considered to scores >6017. The content of this measure pertains to the previous two weeks. The questionnaires are self-fulfillment, with a Likert scale, and validated for the Portuguese population.

Statistical Analysis

Data was analyzed using the IBM SPSS Statistics for Macintosh, Version 28.0 for Mac OS (IBM Corp., Armonk, NY, USA). We present parametric quantitative variables as mean ± standard deviation, non-parametric data as median (minimum-maximum), and categorical variables as percentages. We verified the data’s normal distribution of continuous variables with the Kolmogorov-Smirnov test and performed group comparisons using the t-student or chi-square tests. Metabolic control variables were dichotomized based on target values established for adults with T1DM12,13. We conducted bivariate correlations using Pearson correlation and Spearman correlation tests. Internal consistency analysis was performed for the applied questionnaires using the Cronbach’s Alpha test to assess reliability. We considered p-values <0.05 (two-tailed) statistically significant. 

Results

A sample of 56 participants was obtained. The mean age was 41.2 ± 14.6. The majority were female (58.9 %) and of medium-high socioeconomic class (64.3 %) with a long duration of T1DM diagnosis (21.0 ± 14.6 years), mostly exceeding 10 years (71.4 %). Regarding the type of insulin treatment, 55.4% patients were on multiple daily insulin injections (MDII), while 44.6 % were under continuous subcutaneous insulin infusion (CSII), without integrated CGM. Thirty-nine percent of patients had at least one microvascular complication, 33.9 % retinopathy, 14.3 % nephropathy, 7.1 % neuropathy, and 7.1 % had at least one macrovascular complication.

Metabolic control

The mean HbA1c was 8.0 ± 1.4 %, with 67.9 % of individuals with HbA1c ≥7.0 %. The average percentage of TIR was 52 ± 22 %, with 26.8 % of patients with TIR ≥70 % and 71.4 % below the recommended threshold. TIR correlated negatively with HbA1c (r =-0.631, p <0.001). The mean CV was 37 ± 8 %, above the recommended maximum of 36 %. In individuals with HbA1c <7.0 %, the mean CV was within the recommended range (33.5 ± 8.5 % vs. 39.8 ± 7.3 %, p =0.01). Also, there was a trend for a lower CV in individuals with TIR ≥70 % (34.4 ± 8.1 % vs. 38.9 ± 8 %, p =0.082). About TBR, median was 2 % and 64.7 % had recommended TBR of <4 %. A positive correlation was found between CV and TBR (ρ =0.643, p <0.001). Individuals on CSII spent an average of 13.03 % more TIR than those using MII, and the difference is significant (59.2 ± 17.9 % vs. 46.1 ± 24.1 %, p =0.03). However, no differences were observed in HbA1c between treatment groups (p =0.86).

Psychological adjustment to diabetes

The Cronbach’s Alpha test value obtained was 0.823, indicating good internal consistency for the scale. The percentages of responses for each question based on factors related to psychological adjustment to diabetes18 are presented in Table 1. The mean value obtained for psychological adjustment to diabetes ATT18 was 62.9 ± 11.6, slightly above the median for better adjustment, 60. About 63 % of individuals had an ATT18 score ≥60, revealing good psychological adjustment to diabetes, contrasting with 37.5 % below the cut-point. No significant association was found between psychological adjustment to diabetes (ATT18) and the type of treatment, as well as metabolic control (p =0.63). 

WHO-5 and PHQ-9

The Cronbach’s Alpha test values obtained for PHQ-9 and WHO-5 questionnaires were 0.857 and 0.939, indicating good and very good internal consistency. The mean score obtained in the PHQ-9 questionnaire was 6.6 ± 5.9, below the cutoff of 10, indicating the absence of depression, and a PHQ-9 ≥10 was observed in 17.9 %. The mean score for the WHO-5 index was 58.5 ± 26.4, and a score <50 was identified in 33.9 %.

PHQ-9 correlated inversely with WHO-5 index scores (r =-0.694, p <0.001). A PHQ-9 score ≥10 points (depression) was observed in 17.9 % of participants.

A negative correlation between psychological adjustment to diabetes (ATT18) and PHQ-9 (r =-0.676, p <0.001) was identified. Psychological adjustment to diabetes was better in participants with no suggested depression (PHQ-9 <10) (65.3 ± 9.9 vs. 51.8 ± 12.8, p <0.001). The psychological adjustment to diabetes also positively correlated with the WHO-5 well-being index (r =0.511, p <0.001). No significant relation was observed between the WHO-5 and PHQ-9 and metabolic control.

Discussion

In this study, the majority of patients had HbA1c ≥7.0 % and TIR <70 %, falling outside the recommended target values12,13. This may be justifiable given the complexity of disease management and the significant level of responsibility for glycemic control placed on the patient themselves3,5. Besides that, despite many individuals being on CSII, these devices do not have integrated CGM (“hybrid insulin devices”), which continues to make self-management of glycemic control complex. This aspect reinforces the importance of personalized healthcare and a patient-centered approach to optimize metabolic control and prevent diabetes complications19. To achieve this, it is crucial to incorporate into the clinical assessment not only the monitoring of HbA1c but also the Ambulatory Glucose Profile, including CGM indicators such as TIR, TBR and CV12,13. Additionally, monitoring psychological well-being, quality of life, and attitude toward diabetes should be included, as their association with metabolic control has been demonstrated1,3,5,9.

HbA1c is unquestionably a key marker in clinical practice for assessing metabolic control. However, it only reflects the average glycemia over the past 2-3 months, presenting some limitations in the individualized management of the patient due to the lack of information on glycemic dynamics13. Therefore, including parameters related to glycemic variation obtained through CGM systems is an excellent complement to improving the quality of metabolic assessment. These parameters provide a direct measure of glycemic variability that is easily interpretable by both patients and healthcare professionals and can facilitate individualized therapeutic decision-making, adjustments to insulin dosage, lifestyle changes, and the prevention of complications12,13. This study reinforces this idea, as significant associations were observed between CGM parameters such as TIR, TBR, and CV, as well as the important association between TIR and HbA1c and type of treatment. The relationship between TIR and HbA1c has been widely demonstrated, indicating that time spent within the recommended glycemic range significantly reduces HbA1c20-22. A similar association is observed in the present study, given the significant negative correlation found between TIR and HbA1c. Also, a significant association between CV and TBR was identified, indicating that lower glycemic variability is associated with a lower TBR. This finding is consistent with previous studies that suggested a positive association between the frequency of hypoglycemia and glycemic instability, as higher CV increases the risk of hypoglycemia23,24. Nevertheless, it was observed that patients with HbA1c <7.0 % had lower CV, suggesting that glycemic stability is also related to lower HbA1c24. Regarding the type of treatment, it was observed that patients on CSII spent more TIR than those with MDII. It can be explained by the greater glycemic stability provided by this type of treatment, which closely mimics physiological pancreatic function, is associated with a lower risk of hypoglycemia, and allows for more precise therapeutic adjustments based on the patient’s needs25. In addition to a lower risk of hypoglycemia and no need for daily injections, CSII allows greater flexibility in lifestyle without compromising the quality of life for these individuals26. However, no significant differences were found in the HbA1c values between treatment groups (p =0.86).

Regarding the PROMs, most participants demonstrated good psychological adjustment to diabetes (ATT18 mean score >60) since the majority agreed that diabetes had not changed their life, had hope in leading an everyday life, and believed they could control diabetes, revealing a proactive approach to disease management (Table 1). These findings indicate increased flexibility and tolerance toward ambiguity and anxiety associated with the challenges in diabetes management18. Additionally, the limited perception of change in their lives due to diabetes can be explained by the prolonged time since diagnosis and the fact that their life choices have not been altered due to having diabetes4. However, many respondents consider that diabetes control entails significant sacrifice and inconvenience, which can be justified given the substantial responsibility of daily self-management of therapy, additional care regarding diet and lifestyle, and the resultant impact on daily life1,3,4. Negative feelings towards diabetes, such as guilt and embarrassment, were reported by only a small number of participants (Table 1). The majority also expressed satisfaction with medical care and considered that there exists understanding from healthcare professionals in diabetes treatment. However, nearly 30 % believed that doctors should be more understanding in treating people with diabetes, revealing there is an opportunity for improvement in this regard. The satisfaction with healthcare professionals also reflects a more independent attitude in diabetes management18. Nevertheless, it is important to note that more than half considered that, in general, they have not adapted well to diabetes and yet still appreciate receiving feedback about poor control. This underscores the need to focus our efforts on understanding a patient’s life experience in the context of their pathology, determining the psychological adjustment, well-being, and depression3,8,17.

Most participants demonstrate a favorable emotional state characterized by a predominantly positive attitude. Despite the low incidence of depressive symptoms, as indicated by PHQ-9, and predominantly favorable indicators of well-being given by WHO-5 results, the presence of depression was identified in 17.9 %, as well as suboptimal well-being in 33.9 %. According to previous studies, it is recommended to conduct further testing for depression in these patients, and these issues must be taken into account in patient management9. The applicability of both questionnaires has been demonstrated in the assessment of patients with diabetes, showing good sensitivity and specificity in identifying depressive symptoms in this group of patients9,14,15. The negative correlation between the WHO-5 well-being index and depression scores (PHQ-9) confirms concurrent validity between both tests in this study, similar to the findings of Hajos et al9. Additionally, ATT18 correlated inversely with PHQ-9 and directly with the WHO-5 index, suggesting that patients with better psychological adjustment to diabetes experience fewer symptoms of depression and enhanced well-being, indicating that lower distress aligns with better psychological adjustment. This highlights the importance of promoting behaviors in patients with T1DM that help overcome depression, enhance well-being, and facilitate adaptation to diabetes17.

It is considered that the present study contributes to the existing knowledge regarding patients with T1DM concerning their psychological adjustment to diabetes, quality of life, and metabolic control. However, such studies remain scarce, highlighting the need to further understand this group of patients according to their concerns to provide better healthcare. Therefore, this study emphasizes the importance of investigating CGM metrics and their utilization in clinical assessment, facilitating the necessary adjustments in a personalized way and making it more comfortable and intuitive to interpret for patients with diabetes. The fragilities of this study include the small sample size and the use of a convenience sample, which may compromise the statistical significance of the data and lead to limitations in the generalization of some obtained results. Therefore, the need for future research persists, particularly prospective studies that can better ascertain whether psychological adjustment, well-being, and quality of life indeed influence the ability to control diabetes, since the present study did not allow for the demonstration of a significant relationship between these factors.

Conclusion

In addition to the significant association between CGM metrics, HbA1c, and type of treatment, we identified that patients with positive adaptation to diabetes tend to experience fewer symptoms of depression and enjoy improved well-being. This suggests that reduced distress corresponds to better psychological adjustment. These results underscore the importance of preventing depression, promoting well-being, and enhancing psychological adjustment in this group, aiming to improve the quality of life. According to the results, to provide quality healthcare and a patient-centered approach, it is important to include PROMs as indicators of the well-being and quality of life, as well as psychological adjustment to diabetes, in assessing patients with diabetes alongside the detailed metabolic evaluation.

Conflict of interest

The authors declare no conflict of interest. 

Acknowledgements

We thank Professor Carlos Góis, Department of Psychiatry, Faculty of Medicine, University of Lisbon and Santa Maria Hospital, Lisbon, for kindly providing the Psychological Adjustment to Diabetes Scale (ATT18) for this study. We also thank the patients who accepted to participate in this study.

This research was presented as a poster at the 20th Portuguese Congress of Diabetes in March 2024. 

References

  1. Alvarado-Martel D, Velasco R, Sánchez-Hernández RM, Carrillo A, Nóvoa FJ, Wägner AM. Quality of life and type 1 diabetes: a study assessing patients’ perceptions and self-management needs. Patient Prefer Adherence. 2015; 9: 1315-1323.
  2. DiMeglio LA, Evans-Molina C, Oram RA. Type 1 diabetes. Lancet. 2018; 391: 2449-2462.
  3. Góis C, Santos AL, Silva M, Sousa F, Ferro A, Ouakinin S, et al. O ATT18: Versão portuguesa de um questionário de ajustamento psicológico à Diabetes Mellitus. Psiquiatr Clínica. 2006; 27: 71-81.
  4. Gois CJ, Ferro AC, Santos AL, Sousa FP, Ouakinin SR, do Carmo I, et al. Psychological adjustment to diabetes mellitus: highlighting self-integration and self-regulation. Acta Diabetol. 2012; 49: 33-40.
  5. Wilmot EG, Close KL, Jurišić-Eržen D, Bruttomesso D, Ampudia-Blasco FJ, Bosnyak Z, et al. Patient-reported outcomes in adults with type 1 diabetes in global real-world clinical practice: The SAGE study. Diabetes Obes Metab. 2021; 23: 1892-1901.
  6. Churruca K, Pomare C, Ellis LA, Long JC, Henderson SB, Murphy LED, et al. Patient-reported outcome measures (PROMs): A review of generic and condition-specific measures and a discussion of trends and issues. Health Expect. 2021; 24: 1015-1024.
  7. Nano J, Carinci F, Okunade O, Whittaker S, Walbaum M, Barnard-Kelly K, et al. A standard set of person-centred outcomes for diabetes mellitus: results of an international and unified approach. Diabet Med. 2020; 37: 2009-2018.
  8. Martin-Delgado J, Guilabert M, Mira-Solves J. Patient-Reported Experience and Outcome Measures in People Living with Diabetes: A Scoping Review of Instruments. Patient. 2021; 14: 759-773.
  9. Hajos TR, Pouwer F, Skovlund SE, Den Oudsten BL, Geelhoed-Duijvestijn PH, Tack CJ, et al. Psychometric and screening properties of the WHO-5 well-being index in adult outpatients with Type 1 or Type 2 diabetes mellitus. Diabet Med. 2013; 30: e63-e69.
  10. Renard E, Ikegami H, Daher Vianna AG, Pozzilli P, Brette S, Bosnyak Z, et al. The SAGE study: Global observational analysis of glycaemic control, hypoglycaemia and diabetes management in T1DM. Diabetes Metab Res Rev. 2021; 37: e3430.
  11. Serrabulho L, de Matos MG, Nabais JV, Raposo JF. A Adaptação Psicológica à Diabetes dos Jovens Adultos com Diabetes Tipo 1. Revista Portuguesa de Diabetes. 2014; 9: 116-126.
  12. ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. 6. Glycemic Targets: Standards of Care in Diabetes-2023. Diabetes Care. 2023; 46: S97-S110.
  13. Battelino T, Danne T, Bergenstal RM, Amiel SA, Beck R, Biester T, et al. Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range. Diabetes Care. 2019; 42: 1593-1603.
  14. Topp CW, Østergaard SD, Søndergaard S, Bech P. The WHO-5 Well-Being Index: a systematic review of the literature. Psychother Psychosom. 2015; 84: 167-176.
  15. Costantini L, Pasquarella C, Odone A, Colucci ME, Costanza A, Serafini G, et al. Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): A systematic review. J Affect Disord. 2021; 279: 473-483.
  16. He C, Levis B, Riehm KE, Saadat N, Levis AW, Azar M, et al. The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis. Psychother Psychosom. 2020; 89: 25-37.
  17. Gois C, Barbosa A, Ferro A, Santos AL, Sousa F, Akiskal H, et al. The role of affective temperaments in metabolic control in patients with type 2 diabetes. J Affect Disord. 2011; 134: 52-58.
  18. Welch G, Dunn SM, Beeney L. The ATT39: a measure of patient attitudes to diabetes. Handbook of Psychological and Diabetes. Harwood Academic Publishers, Singapore, 1994, 223-245.
  19. Saisho Y. Use of Diabetes Treatment Satisfaction Questionnaire in Diabetes Care: Importance of Patient-Reported Outcomes. Int J Environ Res Public Health. 2018; 15: 947.
  20. Beck RW, Bergenstal RM, Cheng P, Kollman C, Carlson AL, Johnson ML, et al. The Relationships Between Time in Range, Hyperglycemia Metrics, and HbA1c. J Diabetes Sci Technol. 2019; 13: 614-626.
  21. El Malahi A, Van Elsen M, Charleer S, Dirinck E, Ledeganck K, Keymeulen B, et al. Relationship Between Time in Range, Glycemic Variability, HbA1c, and Complications in Adults With Type 1 Diabetes Mellitus. J Clin Endocrinol Metab. 2022; 107: e570-e581.
  22. Vigersky RA, McMahon C. The Relationship of Hemoglobin A1C to Time-in-Range in Patients with Diabetes. Diabetes Technol Ther. 2019; 21: 81-85.
  23. Rodbard D. Glucose Time In Range, Time Above Range, and Time Below Range Depend on Mean or Median Glucose or HbA1c, Glucose Coefficient of Variation, and Shape of the Glucose Distribution. Diabetes Technol Ther. 2020; 22: 492-500.
  24. Škrha J, Šoupal J, Škrha J Jr, Prázný M. Glucose variability, HbA1c and microvascular complications. Rev Endocr Metab Disord. 2016; 17: 103-110.
  25. Abraham MB, de Bock M, Smith GJ, Dart J, Fairchild JM, King BR, et al. Effect of a Hybrid Closed-Loop System on Glycemic and Psychosocial Outcomes in Children and Adolescents With Type 1 Diabetes: A Randomized Clinical Trial. JAMA Pediatr. 2021; 175: 1227-1235.
  26. Figueiredo AR, Matos T, do Vale S. Monitoring of continuous subcutaneous insulin infusion treatment in Portugal and its implications for diabetes management. Hormones (Athens). 2023; 22: 87-94.