MD (Catholic University of Chile), MSc nutrition (University of Chile), MSc clinical epidemiology (Erasmus University Rotterdam) , PhD in Public Health (University of Liège).
Objectives Diabetes distress can negatively affect the well-being of individuals with type 1 diab... more Objectives Diabetes distress can negatively affect the well-being of individuals with type 1 diabetes (T1D). Voice-based (VB) technology can be used to develop inexpensive and ecological tools for managing diabetes distress. This study explored the competencies to engage with digital health services, needs and preferences of individuals with T1D or caring for a child with this condition regarding VB technology to inform the tailoring of a co-designed tool for supporting diabetes distress management. Design We used a mixed methods design. We performed a qualitative reflexive thematic analysis of semistructured interviews of people living with T1D or caring for a child with T1D, complemented by quantitative analysis (descriptive statistics). Setting 12 adults living with T1D who attended diabetes centres or cared for a child with this condition participated in semistructured interviews to collect opinions about voice technology. They also responded to three questionnaires on sociodemographics and diabetes management, diabetes distress and e-health literacy. Outcome measures Main: Patient experiences and perceptions derived from the coded transcriptions of interview data. Secondary: Quantitative data generated from Socio-Demographic and Diabetes Management questionnaire; Problem Areas in Diabetes Scale and e-Health Literacy Questionnaire. Results Five major themes were generated from the participants’ interview responses: (1) Experience of T1D, (2) Barriers to VB technology use, (3) Facilitators of VB technology, (4) Expectations of VB technology management in T1D, (5) Role of healthcare professionals in implementing VB technology for T1D. Most participants expressed a favourable view of voice technology for diabetes distress management. Trust in technology and healthcare professionals emerged as the predominant sentiment, with participants’ current device type impacting anticipated barriers to adopting new technologies. Conclusion The results highlighted positive participant views towards VB technology. Device use, previous experience and health professional endorsement were influential facilitators of novel VB digital health solutions.
Aims: Diabetes distress (DD) is prevalent among people with diabetes. While automated insulin del... more Aims: Diabetes distress (DD) is prevalent among people with diabetes. While automated insulin delivery systems (AIDs) improve glycemic control, their impact on DD is unclear. We aimed to investigate the effect of AIDs on DD in people with diabetes and their caregivers. Methods: We focused on people with diabetes using AIDs versus other insulin delivery systems, with DD as the outcome. We included randomised controlled trials (RCTs), before-after studies (BAS), and observational studies until April 4, 2024. After screening, 40 studies were included in the systematic review, comprising 5,426 participants (3,210 adults, 1,131 paediatric, and 1,085 caregivers). Twenty-seven studies were selected for the meta-analysis (focusing solely on type 1 diabetes). We used random effects models by population and study design. We also conducted a subgroup analysis by age group (children vs teenagers). Results: In adults, eight BAS and five RCTs indicated a significant small DD reduction post-AID initiation (standardised mean difference (95% confidence intervals) -0.32 (95% CI: -0.40, -0.24) and (-0.19 (-0.27, -0.11)). No significant changes were observed in the paediatric population. In caregivers, eleven BAS and five RCTs indicated a significant moderate DD reduction (-0.48 (95% CI: -0.78, -0.18) and (-0.22 (-0.38, -0.06)). Subgroup analysis revealed an increased benefit in parents of children compared to parents of teenagers. Conclusions: This work suggests that AIDs is associated with a DD reduction in adults and caregivers but not in children/teenagers with type 1 diabetes. More longitudinal studies and better systematic DD assessments are needed.
Aims: To compare impact of pump treatment and continuous glucose
monitoring (CGM) with predictive... more Aims: To compare impact of pump treatment and continuous glucose monitoring (CGM) with predictive low glucose suspend (SmartGuard) or user initiated CGM (iscCGM) on sleep and hypoglycemia fear in children with type 1 Diabetes and parents. Methods: Secondary analysis of data from 5 weeks pump treatment with iscCGM (A) or SmartGuard (B) open label, single center, randomized cross-over study was performed. At baseline and end of treatment arms, sleep and fear of hypoglycemia were evaluated using ActiGraph and questionnaires. Results: 31 children (6-14 years, male: 50%) and 30 parents (28-55 years) participated. Total sleep minutes did not differ significantly for children (B vs. A: -9.27; 95% CI [-24.88; 6.34]; p 0.26) or parents (B vs. A: 5.49; 95% CI [-8.79; 19.77]; p 0.46). Neither daytime sleepiness nor hypoglycemia fear in children or parents differed significantly between the systems. Neither group met recommended sleep criteria. Conclusion: Lack of sleep and fear of hypoglycemia remain a major burden for children with diabetes and their parents. Whilst no significant differences between the systems were found, future technology should consider psychosocial impacts of diabetes and related technologies on children and parents’ lived experience to ensure parity of esteem between physical and mental health outcomes.
The pressing need to reduce undiagnosed type 2 diabetes (T2D) globally calls for innovative scree... more The pressing need to reduce undiagnosed type 2 diabetes (T2D) globally calls for innovative screening approaches. This study investigates the potential of using a voice-based algorithm to predict T2D status in adults, as the first step towards developing a non-invasive and scalable screening method. We analyzed pre-specified text recordings from 607 US participants from the Colive Voice study registered on ClinicalTrials.gov (NCT04848623). Using hybrid BYOL-S/CvT embeddings, we constructed gender-specific algorithms to predict T2D status, evaluated through cross-validation based on accuracy, specificity, sensitivity, and Area Under the Curve (AUC). The best models were stratified by key factors such as age, BMI, and hypertension, and compared to the American Diabetes Association (ADA) score for T2D risk assessment using Bland-Altman analysis. The voice-based algorithms demonstrated good predictive capacity (AUC = 75% for males, 71% for females), correctly predicting 71% of male and 66% of female T2D cases. Performance improved in females aged 60 years or older (AUC = 74%) and individuals with hypertension (AUC = 75%), with an overall agreement above 93% with the ADA risk score. Our findings suggest that voice-based algorithms could serve as a more accessible, cost-effective, and noninvasive screening tool for T2D. While these results are promising, further validation is needed, particularly for early-stage T2D cases and more diverse populations.
Introduction & objectives To evaluate whether cardiovascular risk factors and their management di... more Introduction & objectives To evaluate whether cardiovascular risk factors and their management differ in primary prevention between adult males and females with type 1 diabetes (T1D) in two European countries in 2020-2022 and sex inequalities in achievement of standards of care in diabetes. Methods We used 2020-2022 data of patients without a cardiovascular history in the Prospective Diabetes Follow-up registry (DPV) centres, in Germany, and the Société Francophone du Diabète-Cohorte Diabète de Type 1 cohort (SFDT1), in France.
Background: People living with Long COVID (PWLC), which is still a poorly understood disease, oft... more Background: People living with Long COVID (PWLC), which is still a poorly understood disease, often face major issues accessing proper care and frequently feel abandoned by the healthcare system. PWLC frequently report impaired quality of life because of the medical burden, the variability and intensity of symptoms, and insecurity toward the future. These particular needs justify the development of innovative, minimally disruptive solutions to facilitate the monitoring of this complex and fluctuating disease. Voice-based interactions and vocal biomarkers are promising digital approaches for such health monitoring. Methods: Based on a mixed-method approach, this study describes the entire co-design process of Long COVID Companion, a voice-based digital health app to monitor Long COVID symptoms. Potential end-users of the app, both PWLC and healthcare professionals (HCP) were involved to (1) understand the unmet needs and expectations related to Long COVID care and management, (2) to assess the barriers and facilitators regarding a health monitoring app, (3) to define the app characteristics, including future potential use of vocal biomarkers and (4) to develop a first version of the app. Results: This study revealed high needs and expectations regarding a digital health app to monitor Long COVID symptoms and the readiness to use vocal biomarkers from end-users. The main expectations included improved care and daily life, and major concerns were linked to accessibility and data privacy. Long COVID Companion was developed as a web application and is composed of a health monitoring component that allows auto-evaluation of symptoms, global health, and scoring relevant symptoms and quality of life using standardized questionnaires. Conclusions: The Long COVID Companion app will address a major gap and provide day-today support for PWLC. However, further studies will be needed following its release, to evaluate its acceptability, usability and effectiveness.
Background: The evolution of ulcerative colitis in pregnancy is far from clear. While some author... more Background: The evolution of ulcerative colitis in pregnancy is far from clear. While some authors state that the disease aggravates during this period, others do not share this opinion. Aim: To assess the evolution of ulcerative colitis in pregnancy. Patients and methods: A paired case-control design was used in which 15 women, with diagnosed ulcerative colitis at the moment of becoming pregnant, were followed for 12 months and the activity of the disease was compared with that of the 12 months preceding the pregnancy. The activity of the disease in the period preceding the pregnancy was gathered retrospectively from the patient's charts. Results: The mean age of the first ulcerative colitis crisis was 24 years. It was moderate in 49% and severe in 35% of women. During pregnancy 55% of women did not have a crisis, compared with 26.7% during the period preceding pregnancy (relative risk of not having a crisis during pregnancy of 1.7). During both periods, seven women had digestive complications, whereas extra digestive complications were not observed in 60% of patients during pregnancy and 11.8% of patients during the preceding period. Perinatal results were similar to those of the general population. Conclusions: In our group of patients the evolution of ulcerative colitis was better during pregnancy, reflected by a lower number of crisis.
CGM-derived data are usually analyzed in silos, metric by metric, for diabetes care or therapeuti... more CGM-derived data are usually analyzed in silos, metric by metric, for diabetes care or therapeutic strategy. Simultaneous analysis of the various dimensions of glycemic control would allow a more comprehensive overview. We analyzed data of people with type 1 diabetes (PWT1D) in the SFDT1 cohort study. A K-means clustering model was developed with HbA1c, coefficient of variation (CV), time in range (TIR), time below 70 mg/dl (TBR), Gold score and Glycemic Risk Index (GRI). We included 618 participants (53% men, age 41±14 years) from 20 centres in France. The optimal model had 3 clusters. The “Euglycemia” cluster (n=280, 45%) was characterized by a high TIR (mean 69.3%); low TBR (4.2%), TAR (time above 180 mg/dl: 26.5%), CV (35.7%), HbA1c (7.0%) and GRI (36.4) values. The “Hyperglycaemia" cluster (n=197, 32%) was characterized by high TAR (56.5%), GRI (71.8) and HbA1c (8.5%) and low TIR (40.3%), TBR (3.2%) and Gold score (2.3). The “Hypoglycemia” cluster (n=141, 23%) was characterized by high TBR (15.8%), CV (46.1%), GRI (69.2) and Gold score (3.0). Participants in the “Hyperglycemia” cluster were younger, socially vulnerable, and had more frequent hypertension than those in the “Euglycemia” cluster. To conclude, we identified three distinct, clinically relevant glycemic control profiles, enabling better characterization of PWT1D and, thus, more personalised management. Disclosure E.Cosson: Board Member; Abbott, Medtronic. G.A.Aguayo: None. L.Sablone: None. M.Huet: None. J.Riveline: Board Member; Abbott Diabetes, Medtronic, Dexcom, Inc., Novo Nordisk, Eli Lilly and Company, Sanofi. G.Fagherazzi: Advisory Panel; Roche Diabetes Care, Consultant; AbbVie Inc., Lilly. Sfdt1 study team: n/a.
IntroductionType 1 diabetes (T1D) requires continuous management to obtain a good metabolic contr... more IntroductionType 1 diabetes (T1D) requires continuous management to obtain a good metabolic control and prevent acute complications. This often affects psychological well-being. People with T1D frequently report diabetes distress (DD). Psychological issues can negatively affect metabolic control and well-being. New technologies can improve quality of life, reduce the treatment burden and improve glycaemic control. Voice technology may serve as an innovative and inexpensive remote monitoring device to evaluate psychological well-being. Tailoring digital health interventions according to the ability and interest of their intended ‘end-users’ increases the acceptability of the intervention itself. PsyVoice explores the perspectives and needs of people with T1D on voice-based digital health interventions to manage DD.Methods and analysisPsyVoice is a mixed-methods study with qualitative and quantitative data sources. For the qualitative part, the researchers will invite 20 people with a...
Objectives Diabetes distress can negatively affect the well-being of individuals with type 1 diab... more Objectives Diabetes distress can negatively affect the well-being of individuals with type 1 diabetes (T1D). Voice-based (VB) technology can be used to develop inexpensive and ecological tools for managing diabetes distress. This study explored the competencies to engage with digital health services, needs and preferences of individuals with T1D or caring for a child with this condition regarding VB technology to inform the tailoring of a co-designed tool for supporting diabetes distress management. Design We used a mixed methods design. We performed a qualitative reflexive thematic analysis of semistructured interviews of people living with T1D or caring for a child with T1D, complemented by quantitative analysis (descriptive statistics). Setting 12 adults living with T1D who attended diabetes centres or cared for a child with this condition participated in semistructured interviews to collect opinions about voice technology. They also responded to three questionnaires on sociodemographics and diabetes management, diabetes distress and e-health literacy. Outcome measures Main: Patient experiences and perceptions derived from the coded transcriptions of interview data. Secondary: Quantitative data generated from Socio-Demographic and Diabetes Management questionnaire; Problem Areas in Diabetes Scale and e-Health Literacy Questionnaire. Results Five major themes were generated from the participants’ interview responses: (1) Experience of T1D, (2) Barriers to VB technology use, (3) Facilitators of VB technology, (4) Expectations of VB technology management in T1D, (5) Role of healthcare professionals in implementing VB technology for T1D. Most participants expressed a favourable view of voice technology for diabetes distress management. Trust in technology and healthcare professionals emerged as the predominant sentiment, with participants’ current device type impacting anticipated barriers to adopting new technologies. Conclusion The results highlighted positive participant views towards VB technology. Device use, previous experience and health professional endorsement were influential facilitators of novel VB digital health solutions.
Aims: Diabetes distress (DD) is prevalent among people with diabetes. While automated insulin del... more Aims: Diabetes distress (DD) is prevalent among people with diabetes. While automated insulin delivery systems (AIDs) improve glycemic control, their impact on DD is unclear. We aimed to investigate the effect of AIDs on DD in people with diabetes and their caregivers. Methods: We focused on people with diabetes using AIDs versus other insulin delivery systems, with DD as the outcome. We included randomised controlled trials (RCTs), before-after studies (BAS), and observational studies until April 4, 2024. After screening, 40 studies were included in the systematic review, comprising 5,426 participants (3,210 adults, 1,131 paediatric, and 1,085 caregivers). Twenty-seven studies were selected for the meta-analysis (focusing solely on type 1 diabetes). We used random effects models by population and study design. We also conducted a subgroup analysis by age group (children vs teenagers). Results: In adults, eight BAS and five RCTs indicated a significant small DD reduction post-AID initiation (standardised mean difference (95% confidence intervals) -0.32 (95% CI: -0.40, -0.24) and (-0.19 (-0.27, -0.11)). No significant changes were observed in the paediatric population. In caregivers, eleven BAS and five RCTs indicated a significant moderate DD reduction (-0.48 (95% CI: -0.78, -0.18) and (-0.22 (-0.38, -0.06)). Subgroup analysis revealed an increased benefit in parents of children compared to parents of teenagers. Conclusions: This work suggests that AIDs is associated with a DD reduction in adults and caregivers but not in children/teenagers with type 1 diabetes. More longitudinal studies and better systematic DD assessments are needed.
Aims: To compare impact of pump treatment and continuous glucose
monitoring (CGM) with predictive... more Aims: To compare impact of pump treatment and continuous glucose monitoring (CGM) with predictive low glucose suspend (SmartGuard) or user initiated CGM (iscCGM) on sleep and hypoglycemia fear in children with type 1 Diabetes and parents. Methods: Secondary analysis of data from 5 weeks pump treatment with iscCGM (A) or SmartGuard (B) open label, single center, randomized cross-over study was performed. At baseline and end of treatment arms, sleep and fear of hypoglycemia were evaluated using ActiGraph and questionnaires. Results: 31 children (6-14 years, male: 50%) and 30 parents (28-55 years) participated. Total sleep minutes did not differ significantly for children (B vs. A: -9.27; 95% CI [-24.88; 6.34]; p 0.26) or parents (B vs. A: 5.49; 95% CI [-8.79; 19.77]; p 0.46). Neither daytime sleepiness nor hypoglycemia fear in children or parents differed significantly between the systems. Neither group met recommended sleep criteria. Conclusion: Lack of sleep and fear of hypoglycemia remain a major burden for children with diabetes and their parents. Whilst no significant differences between the systems were found, future technology should consider psychosocial impacts of diabetes and related technologies on children and parents’ lived experience to ensure parity of esteem between physical and mental health outcomes.
The pressing need to reduce undiagnosed type 2 diabetes (T2D) globally calls for innovative scree... more The pressing need to reduce undiagnosed type 2 diabetes (T2D) globally calls for innovative screening approaches. This study investigates the potential of using a voice-based algorithm to predict T2D status in adults, as the first step towards developing a non-invasive and scalable screening method. We analyzed pre-specified text recordings from 607 US participants from the Colive Voice study registered on ClinicalTrials.gov (NCT04848623). Using hybrid BYOL-S/CvT embeddings, we constructed gender-specific algorithms to predict T2D status, evaluated through cross-validation based on accuracy, specificity, sensitivity, and Area Under the Curve (AUC). The best models were stratified by key factors such as age, BMI, and hypertension, and compared to the American Diabetes Association (ADA) score for T2D risk assessment using Bland-Altman analysis. The voice-based algorithms demonstrated good predictive capacity (AUC = 75% for males, 71% for females), correctly predicting 71% of male and 66% of female T2D cases. Performance improved in females aged 60 years or older (AUC = 74%) and individuals with hypertension (AUC = 75%), with an overall agreement above 93% with the ADA risk score. Our findings suggest that voice-based algorithms could serve as a more accessible, cost-effective, and noninvasive screening tool for T2D. While these results are promising, further validation is needed, particularly for early-stage T2D cases and more diverse populations.
Introduction & objectives To evaluate whether cardiovascular risk factors and their management di... more Introduction & objectives To evaluate whether cardiovascular risk factors and their management differ in primary prevention between adult males and females with type 1 diabetes (T1D) in two European countries in 2020-2022 and sex inequalities in achievement of standards of care in diabetes. Methods We used 2020-2022 data of patients without a cardiovascular history in the Prospective Diabetes Follow-up registry (DPV) centres, in Germany, and the Société Francophone du Diabète-Cohorte Diabète de Type 1 cohort (SFDT1), in France.
Background: People living with Long COVID (PWLC), which is still a poorly understood disease, oft... more Background: People living with Long COVID (PWLC), which is still a poorly understood disease, often face major issues accessing proper care and frequently feel abandoned by the healthcare system. PWLC frequently report impaired quality of life because of the medical burden, the variability and intensity of symptoms, and insecurity toward the future. These particular needs justify the development of innovative, minimally disruptive solutions to facilitate the monitoring of this complex and fluctuating disease. Voice-based interactions and vocal biomarkers are promising digital approaches for such health monitoring. Methods: Based on a mixed-method approach, this study describes the entire co-design process of Long COVID Companion, a voice-based digital health app to monitor Long COVID symptoms. Potential end-users of the app, both PWLC and healthcare professionals (HCP) were involved to (1) understand the unmet needs and expectations related to Long COVID care and management, (2) to assess the barriers and facilitators regarding a health monitoring app, (3) to define the app characteristics, including future potential use of vocal biomarkers and (4) to develop a first version of the app. Results: This study revealed high needs and expectations regarding a digital health app to monitor Long COVID symptoms and the readiness to use vocal biomarkers from end-users. The main expectations included improved care and daily life, and major concerns were linked to accessibility and data privacy. Long COVID Companion was developed as a web application and is composed of a health monitoring component that allows auto-evaluation of symptoms, global health, and scoring relevant symptoms and quality of life using standardized questionnaires. Conclusions: The Long COVID Companion app will address a major gap and provide day-today support for PWLC. However, further studies will be needed following its release, to evaluate its acceptability, usability and effectiveness.
Background: The evolution of ulcerative colitis in pregnancy is far from clear. While some author... more Background: The evolution of ulcerative colitis in pregnancy is far from clear. While some authors state that the disease aggravates during this period, others do not share this opinion. Aim: To assess the evolution of ulcerative colitis in pregnancy. Patients and methods: A paired case-control design was used in which 15 women, with diagnosed ulcerative colitis at the moment of becoming pregnant, were followed for 12 months and the activity of the disease was compared with that of the 12 months preceding the pregnancy. The activity of the disease in the period preceding the pregnancy was gathered retrospectively from the patient's charts. Results: The mean age of the first ulcerative colitis crisis was 24 years. It was moderate in 49% and severe in 35% of women. During pregnancy 55% of women did not have a crisis, compared with 26.7% during the period preceding pregnancy (relative risk of not having a crisis during pregnancy of 1.7). During both periods, seven women had digestive complications, whereas extra digestive complications were not observed in 60% of patients during pregnancy and 11.8% of patients during the preceding period. Perinatal results were similar to those of the general population. Conclusions: In our group of patients the evolution of ulcerative colitis was better during pregnancy, reflected by a lower number of crisis.
CGM-derived data are usually analyzed in silos, metric by metric, for diabetes care or therapeuti... more CGM-derived data are usually analyzed in silos, metric by metric, for diabetes care or therapeutic strategy. Simultaneous analysis of the various dimensions of glycemic control would allow a more comprehensive overview. We analyzed data of people with type 1 diabetes (PWT1D) in the SFDT1 cohort study. A K-means clustering model was developed with HbA1c, coefficient of variation (CV), time in range (TIR), time below 70 mg/dl (TBR), Gold score and Glycemic Risk Index (GRI). We included 618 participants (53% men, age 41±14 years) from 20 centres in France. The optimal model had 3 clusters. The “Euglycemia” cluster (n=280, 45%) was characterized by a high TIR (mean 69.3%); low TBR (4.2%), TAR (time above 180 mg/dl: 26.5%), CV (35.7%), HbA1c (7.0%) and GRI (36.4) values. The “Hyperglycaemia" cluster (n=197, 32%) was characterized by high TAR (56.5%), GRI (71.8) and HbA1c (8.5%) and low TIR (40.3%), TBR (3.2%) and Gold score (2.3). The “Hypoglycemia” cluster (n=141, 23%) was characterized by high TBR (15.8%), CV (46.1%), GRI (69.2) and Gold score (3.0). Participants in the “Hyperglycemia” cluster were younger, socially vulnerable, and had more frequent hypertension than those in the “Euglycemia” cluster. To conclude, we identified three distinct, clinically relevant glycemic control profiles, enabling better characterization of PWT1D and, thus, more personalised management. Disclosure E.Cosson: Board Member; Abbott, Medtronic. G.A.Aguayo: None. L.Sablone: None. M.Huet: None. J.Riveline: Board Member; Abbott Diabetes, Medtronic, Dexcom, Inc., Novo Nordisk, Eli Lilly and Company, Sanofi. G.Fagherazzi: Advisory Panel; Roche Diabetes Care, Consultant; AbbVie Inc., Lilly. Sfdt1 study team: n/a.
IntroductionType 1 diabetes (T1D) requires continuous management to obtain a good metabolic contr... more IntroductionType 1 diabetes (T1D) requires continuous management to obtain a good metabolic control and prevent acute complications. This often affects psychological well-being. People with T1D frequently report diabetes distress (DD). Psychological issues can negatively affect metabolic control and well-being. New technologies can improve quality of life, reduce the treatment burden and improve glycaemic control. Voice technology may serve as an innovative and inexpensive remote monitoring device to evaluate psychological well-being. Tailoring digital health interventions according to the ability and interest of their intended ‘end-users’ increases the acceptability of the intervention itself. PsyVoice explores the perspectives and needs of people with T1D on voice-based digital health interventions to manage DD.Methods and analysisPsyVoice is a mixed-methods study with qualitative and quantitative data sources. For the qualitative part, the researchers will invite 20 people with a...
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Papers by Gloria Aguayo
T1D or caring for a child with this condition regarding VB technology to inform the tailoring of a co-designed tool for supporting diabetes distress management. Design We used a mixed methods design. We performed
a qualitative reflexive thematic analysis of semistructured interviews of people living with T1D or caring for a child with T1D, complemented by quantitative analysis (descriptive statistics).
Setting 12 adults living with T1D who attended diabetes centres or cared for a child with this condition participated in semistructured interviews to collect opinions about voice technology. They also responded to
three questionnaires on sociodemographics and diabetes management, diabetes distress and e-health literacy.
Outcome measures Main: Patient experiences and perceptions derived from the coded transcriptions of interview data. Secondary: Quantitative data generated from Socio-Demographic and Diabetes Management
questionnaire; Problem Areas in Diabetes Scale and e-Health Literacy Questionnaire.
Results Five major themes were generated from the participants’ interview responses: (1) Experience of T1D, (2) Barriers to VB technology use, (3) Facilitators of VB technology, (4) Expectations of VB technology
management in T1D, (5) Role of healthcare professionals in implementing VB technology for T1D. Most participants expressed a favourable view of voice technology for diabetes distress management. Trust in technology and healthcare professionals emerged as the predominant sentiment, with participants’ current device type impacting anticipated barriers to adopting new technologies.
Conclusion The results highlighted positive participant views towards VB technology. Device use, previous experience and health professional endorsement were influential facilitators of novel VB digital health solutions.
Methods: We focused on people with diabetes using AIDs versus other insulin delivery systems, with DD as the outcome. We included randomised controlled trials (RCTs), before-after studies (BAS), and observational studies until April 4, 2024. After screening, 40 studies were included in the systematic review, comprising 5,426 participants (3,210 adults, 1,131 paediatric, and 1,085 caregivers). Twenty-seven studies were selected for the meta-analysis (focusing solely on type 1 diabetes). We used random effects models by population and study design. We also conducted a subgroup analysis by age group (children vs teenagers).
Results: In adults, eight BAS and five RCTs indicated a significant small DD reduction post-AID initiation (standardised mean difference (95% confidence intervals) -0.32 (95% CI: -0.40, -0.24) and (-0.19 (-0.27, -0.11)). No significant changes were observed in the paediatric population. In caregivers, eleven BAS and five RCTs indicated a significant moderate DD reduction (-0.48 (95% CI: -0.78, -0.18) and (-0.22 (-0.38, -0.06)). Subgroup analysis revealed an increased benefit in parents of children compared to parents of teenagers.
Conclusions: This work suggests that AIDs is associated with a DD reduction in adults and caregivers but not in children/teenagers with type 1 diabetes. More longitudinal studies and better systematic DD assessments are needed.
monitoring (CGM) with predictive low glucose suspend (SmartGuard) or user initiated CGM (iscCGM) on sleep and hypoglycemia fear in children with type 1 Diabetes and parents.
Methods: Secondary analysis of data from 5 weeks pump treatment with iscCGM (A) or SmartGuard (B) open label, single center, randomized cross-over study was performed. At baseline and end of treatment arms, sleep and fear of hypoglycemia were evaluated using ActiGraph and questionnaires.
Results: 31 children (6-14 years, male: 50%) and 30 parents (28-55 years)
participated. Total sleep minutes did not differ significantly for children (B vs. A: -9.27; 95% CI [-24.88; 6.34]; p 0.26) or parents (B vs. A: 5.49; 95% CI [-8.79; 19.77]; p 0.46). Neither daytime sleepiness nor hypoglycemia fear in children or parents differed significantly between the systems. Neither group met recommended sleep criteria.
Conclusion: Lack of sleep and fear of hypoglycemia remain a major burden for children with diabetes and their parents. Whilst no significant differences between the systems were found, future technology should consider psychosocial impacts of diabetes and related technologies on children and parents’ lived experience to ensure parity of esteem between physical and mental health outcomes.
T1D or caring for a child with this condition regarding VB technology to inform the tailoring of a co-designed tool for supporting diabetes distress management. Design We used a mixed methods design. We performed
a qualitative reflexive thematic analysis of semistructured interviews of people living with T1D or caring for a child with T1D, complemented by quantitative analysis (descriptive statistics).
Setting 12 adults living with T1D who attended diabetes centres or cared for a child with this condition participated in semistructured interviews to collect opinions about voice technology. They also responded to
three questionnaires on sociodemographics and diabetes management, diabetes distress and e-health literacy.
Outcome measures Main: Patient experiences and perceptions derived from the coded transcriptions of interview data. Secondary: Quantitative data generated from Socio-Demographic and Diabetes Management
questionnaire; Problem Areas in Diabetes Scale and e-Health Literacy Questionnaire.
Results Five major themes were generated from the participants’ interview responses: (1) Experience of T1D, (2) Barriers to VB technology use, (3) Facilitators of VB technology, (4) Expectations of VB technology
management in T1D, (5) Role of healthcare professionals in implementing VB technology for T1D. Most participants expressed a favourable view of voice technology for diabetes distress management. Trust in technology and healthcare professionals emerged as the predominant sentiment, with participants’ current device type impacting anticipated barriers to adopting new technologies.
Conclusion The results highlighted positive participant views towards VB technology. Device use, previous experience and health professional endorsement were influential facilitators of novel VB digital health solutions.
Methods: We focused on people with diabetes using AIDs versus other insulin delivery systems, with DD as the outcome. We included randomised controlled trials (RCTs), before-after studies (BAS), and observational studies until April 4, 2024. After screening, 40 studies were included in the systematic review, comprising 5,426 participants (3,210 adults, 1,131 paediatric, and 1,085 caregivers). Twenty-seven studies were selected for the meta-analysis (focusing solely on type 1 diabetes). We used random effects models by population and study design. We also conducted a subgroup analysis by age group (children vs teenagers).
Results: In adults, eight BAS and five RCTs indicated a significant small DD reduction post-AID initiation (standardised mean difference (95% confidence intervals) -0.32 (95% CI: -0.40, -0.24) and (-0.19 (-0.27, -0.11)). No significant changes were observed in the paediatric population. In caregivers, eleven BAS and five RCTs indicated a significant moderate DD reduction (-0.48 (95% CI: -0.78, -0.18) and (-0.22 (-0.38, -0.06)). Subgroup analysis revealed an increased benefit in parents of children compared to parents of teenagers.
Conclusions: This work suggests that AIDs is associated with a DD reduction in adults and caregivers but not in children/teenagers with type 1 diabetes. More longitudinal studies and better systematic DD assessments are needed.
monitoring (CGM) with predictive low glucose suspend (SmartGuard) or user initiated CGM (iscCGM) on sleep and hypoglycemia fear in children with type 1 Diabetes and parents.
Methods: Secondary analysis of data from 5 weeks pump treatment with iscCGM (A) or SmartGuard (B) open label, single center, randomized cross-over study was performed. At baseline and end of treatment arms, sleep and fear of hypoglycemia were evaluated using ActiGraph and questionnaires.
Results: 31 children (6-14 years, male: 50%) and 30 parents (28-55 years)
participated. Total sleep minutes did not differ significantly for children (B vs. A: -9.27; 95% CI [-24.88; 6.34]; p 0.26) or parents (B vs. A: 5.49; 95% CI [-8.79; 19.77]; p 0.46). Neither daytime sleepiness nor hypoglycemia fear in children or parents differed significantly between the systems. Neither group met recommended sleep criteria.
Conclusion: Lack of sleep and fear of hypoglycemia remain a major burden for children with diabetes and their parents. Whilst no significant differences between the systems were found, future technology should consider psychosocial impacts of diabetes and related technologies on children and parents’ lived experience to ensure parity of esteem between physical and mental health outcomes.