Diabetes-Specific Quality of Life Changes Associated with a Digital Support Intervention: A Study of Adults with Type 1 Diabetes
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors The manuscript presents a well-structured study examining diabetes-specific quality of life (DSQoL) changes following a six-month multi-modal digital support intervention for adults with type 1 diabetes (T1D). The study leverages the Type 1 Diabetes And Life (T1DAL) survey to assess outcomes across different age cohorts, providing valuable insights into the effectiveness of digital health interventions. The study is timely and relevant given the increasing role of digital platforms in chronic disease management. While the research is methodologically sound, a few areas require clarification and improvement to enhance the manuscript's rigor, clarity and impact. Strengths The study addresses an important gap in diabetes management by evaluating the impact of a digital intervention on DSQoL using an age-specific instrument. The use of a pre-post study design with validated measures strengthens the credibility of the findings. The manuscript effectively describes the TRIFECTA intervention, its theoretical basis (self-determination theory) and its implementation, which adds to the study’s reproducibility. The stratification by age groups allows for a nuanced understanding of the intervention’s impact across different developmental stages. Areas for improvement The manuscript does not discuss the lack of a control group and how it affects the interpretation of results. Addressing potential biases (e.g., natural variations in DSQoL over time) would strengthen the validity of conclusions. Given the relatively small sample size, especially for subgroup analyses (e.g., 8 participants in the 46-60 age group), results should be interpreted cautiously. A discussion on power analysis or effect sizes would be beneficial. The Wilcoxon Signed-Rank test was used for age group comparisons due to small sample sizes. However, it is unclear why ANOVA was used for overall comparisons instead of a consistent statistical approach across all analyses. Clarifying the rationale for these choices would improve transparency. While DSQoL improvements were found overall, only certain age groups showed statistically significant changes. The manuscript should further explore why the 26-45 age group benefited the most, considering possible confounders such as lifestyle, digital literacy, or diabetes duration. The study reports reduced social isolation in the 46-60 age group but no significant DSQoL changes for participants over 60. The discussion could elaborate on potential reasons, such as differing engagement levels or intervention preferences among older individuals. The sample is predominantly white (73%) and female (75%), limiting the generalizability of findings. A discussion on how demographic skewness affects applicability to broader T1D populations would add value. Participants were recruited via online platforms and social media, which may introduce selection bias favoring digitally literate individuals. Addressing how this might influence results would strengthen the manuscript. The study does not report participant engagement levels with the digital platforms. Including adherence metrics (e.g., frequency of participation in Virtual Huddles or WhatsApp interactions) would help contextualize effectiveness. While the manuscript focuses on quantitative findings, incorporating qualitative insights (e.g., participant testimonials or perceived barriers) could provide richer interpretations of DSQoL changes. Recommendations Clarify study limitations by discussing the absence of a control group, selection bias and small sample size limitations. Provide more detail on engagement and adherence to determine if increased platform interaction correlates with improved DSQoL outcomes. Enhance statistical reporting by justifying the choice of statistical tests and discussing statistical power concerns. Expand the discussion to include possible explanations for differential age group benefits and potential strategies to enhance engagement for older adults. Address generalizability concerns by discussing demographic limitations and their implications for broader T1D populations. The study is well-conceived and makes a valuable contribution to diabetes digital health research. With some refinements in analysis, interpretation and reporting, the manuscript will be significantly strengthened for publication.Author Response
| Comment 1: The manuscript does not discuss the lack of a control group and how it affects the interpretation of results. |
| Response 1:
As suggested by the reviewer, on page 7, lines 214-216, we have added the following text: “First, in the absence of a control group, we are not able to conclude that any improvements observed are directly related to the intervention itself.” |
| Comment 2: Addressing potential biases (e.g., natural variations in DSQoL over time) would strengthen the validity of conclusions. |
| Response 1: We appreciate the reviewer raising this point. Thus, on page 7, lines 216-218, we have added the following text: “Second, potential bias including recruiting a self-selected sample, natural changes in DSQoL over time, testing effect, and confounding variables (rural vs urban residence) may affect the outcome.” |
| Comment 3: Given the relatively small sample size, especially for subgroup analyses (e.g., 8 participants in the 46-60 age group), results should be interpreted cautiously. |
| Response 3: As suggested by the reviewer, on page 7, lines 218-220, we have added the following text: “Third, while using an age band-specific instrument increases specificity, statistical power is reduced substantially, particularly for the 46-60 age cohort (n=8)." |
| Comment 4: A discussion on power analysis or effect sizes would be beneficial. |
| Response 4:
Authors’ response. A post hoc power analysis was conducted and is included in the limitations paragraph of the Discussion. This is listed as a limitation due to high chance of a type II error (not finding significance when it may be present). The authors acknowledge that a larger sample is needed and the findings presented are only preliminary. On page 7, lines 220-225: “Achieved power based on chosen statistical test, sample size, and estimated effect size was only 0.37 indicating high chance of type II error. Thus, results should be interpreted with caution. Finally, because our sample was largely White and female, results cannot be generalized to the larger T1D population nor can results reflect non-lockdown conditions. Future studies should recruit a more sociodemographically diverse sample size across age bands” |
| Comment 5: The Wilcoxon Signed-Rank test was used for age group comparisons due to small sample sizes. However, it is unclear why ANOVA was used for overall comparisons instead of a consistent statistical approach across all analyses. Clarifying the rationale for these choices would improve transparency. |
| Response 5: The initial decision to use ANOVA was based on sample size. Given that the Wilcoxon Signed-Rank test can be appropriately used in this instance and has less statistical assumptions, the authors have substituted the Wilcoxon Signed Rank test for the prior ANOVA. Results were minimally changed and interpretations remain the same. The Statistical Analysis subsection of the Methods and the Results of the revised manuscript have been updated accordingly. On page 3, lines 111-114, it reads: “Nonparametric tests were used given small subsample sizes as these tests have less statistical assumptions about the distribution of scores and provide exact tests based on the sample. All analyses were performed in R version 3.6.2 statistical software (R Core Team, 2020).”
And, on page 4, lines 127-133, it reads: " Table 2 presents the means and standard deviations for the T1DAL at baseline and 6-months. Table 2 also shows the p-values of the Wilcoxon Sign Rank test for the overall T1DAL score, T1DAL scores by age band, and for the subscale scores. As shown in the table, overall T1DAL scores demonstrated a significant increase over time with a small effect size (d = 0.219). T1DAL scores changed more for 26–45-year-old participants, particularly regarding improvements in the Emotional Experiences & Daily Activities subscale of the T1DAL.” |
| Comment 6: While DSQoL improvements were found overall, only certain age groups showed statistically significant changes. The manuscript should further explore why the 26-45 age group benefited the most, considering possible confounders such as lifestyle, digital literacy, or diabetes duration |
| Response 6: As suggested by the reviewer, on page 6-7, lines 181-189: “This finding could be related to having more statistical power due to larger subsample size or due, in part, to differential engagement by age. In fact, the 26-45 year old cohort attended more Huddle sessions compared to their 18-25 year old counterparts. Interestingly, previous research also found higher utilization rates for digital health management interventions among “middle-aged adults” compared to “young adults” [10]. Subsequent research should examine a possible dose response relationship with DSQoL change across age bands with larger subsample sizes to verify this preliminary finding.”
[10] Jeong, S.-H.; Nam, Y.G. The Paradox of Digital Health: Why Middle-Aged Adults Outperform Young Adults in Health Management Utilization via Technology. Healthcare 2024, 12, 2261. https://doi.org/10.3390/healthcare12222261 |
| Comment 7: The study reports reduced social isolation in the 46-60 age group but no significant DSQoL changes for participants over 60. The discussion could elaborate on potential reasons, such as differing engagement levels or intervention preferences among older individuals. |
| Response 7: As suggested, we added the following text on page 7, lines 195-200: “Interestingly, social isolation scores remained unchanged for adults older than 60 years. However, previous research suggests that older adults derive greater meaning from interactions with close friends and family and, thus, may be less reliant on social networks [14,15]. Moreover, because the consequences of isolation and loneliness can be worse for younger adults compared to older adults [16], it is possible that the TRIFECTA intervention had a more positive impact on the former.”
[14] Birditt, K. S., & Fingerman, K. L. (2003). Age and gender differences in adults’ descriptions of emotional reactions to interpersonal problems. The Journals of Gerontology: Series B 2003, 58, 237–245. https://doi.org/10.1093/geronb/58.4.p237 [15] Singh, A., & Misra, N. Loneliness, depression and sociability in old age. Industrial Psychiatry Journal 2009, 18, 51-55. https://doi.org/10.4103/0972-6748.57861 [16] Beam, C. R., & Kim, A. J. Psychological sequelae of social isolation and loneliness might be a larger problem in young adults than older adults. Psychological trauma: theory, research, practice and policy 2020, 12, S58–S60. https://doi.org/10.1037/tra0000774 |
| Comment 8: The sample is predominantly white (73%) and female (75%), limiting the generalizability of findings. A discussion on how demographic skewness affects applicability to broader T1D populations would add value. |
| Response 8: As suggested by the reviewer, on page 7, lines 222-225, we have added the following text: “Finally, because our sample was largely White and female, results cannot be generalized to the larger T1D population nor can results reflect non-lockdown conditions. Future studies should recruit a more sociodemographically diverse sample size across age bands.” |
| Comment 9: Participants were recruited via online platforms and social media, which may introduce selection bias favoring digitally literate individuals. Addressing how this might influence results would strengthen the manuscript. |
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Response 9: As suggested by the reviewer, on page 7, lines 205-213, we added the following text: “Digital support interventions such as TRIFECTA may disproportionately attract younger and more technology savvy end users. In fact, 68% of our sample were between the ages of 18 to 45 years while only 27% were 46 years and older. While research shows that older adults are aware of online resources for mental health support [18], their difficulty navigating mobile apps and websites [18, 19, 20], preference for in-person versus virtual interactions [18, 19], and concerns around privacy and security [19, 21] can potentially produce selection bias in recruitment. However, emphasizing the benefits of interpersonal connection and social support for this age group can overcome these obstacles to engagement [19, 20].” [18] Andrews JA, Brown LJ, Hawley MS, Astell AJ [19] Garcia Reyes EP, Kelly R, Buchanan G, Waycott J [20] Lu SY, Yoon S, Yee WQ, Heng Wen Ngiam N, Ng KYY, Low LL [21] LaMonica HM, Davenport TA, Roberts AE, Hickie IB |
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Comment 10: The study does not report participant engagement levels with the digital platforms. Including adherence metrics (e.g., frequency of participation in Virtual Huddles or WhatsApp interactions) would help contextualize effectiveness. |
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Response 10: The authors included a new subsection of the Results of the revised manuscript (“TRIFECTA Engagement Metrics & Associations with T1DAL Scales”) to address this reviewer’s comment and provide more information about engagement and associations with T1DAL outcomes. On page 4, lines 135-144, it reads: “3.3. TRIFECTA Engagement Metrics & Associations with T1DAL Scales. The descriptive statistics associated with engagement in the TRIFECTA peer support platforms were reported in a prior publication [6]. Descriptive statistics for each platform engagement metric were as follows: Huddle Sessions M=2, SD=2; Ask-the-expert Posts M=1, SD=2; Ask-the-expert Views M=19, SD=18; and WhatsApp Messages M=22, SD=36. Change in overall T1DAL score for 26-45-year-old’s was significantly associated with Ask-the-expert Views (ρ=0.574, B-H adj. p-value = 0.008) and with Huddle Sessions attended (ρ=0.515, B-H adj. p-value = 0.016). Counts of WhatsApp Messages and Ask-the-expert Posts were not associated with T1DAL change scores for this age band. No other associations for other age bands were found to be statistically significant.” [6] Tang, T. S., Seddigh, S., Halbe, E., & Vesco, A. T. Testing 3 digital health platforms to improve mental health outcomes in adults with type 1 diabetes: A pilot trial. Canadian Journal of Diabetes 2024, 48, 18-25. https://doi.org/10.1016/j.jcjd.2023.08.006 |
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Comment 11: While the manuscript focuses on quantitative findings, incorporating qualitative insights (e.g., participant testimonials or perceived barriers) could provide richer interpretations of DSQoL changes. |
| Response 11: We agree with the reviewer that a mixed methods study design incorporating qualitative focus groups or interviews would yield a more comprehensive understanding of participants’ experience and provide insights to why some age groups improved on some DsQoL dimensions but others did not. Unfortunately, the study was designed as a pre-post intervention study with only a quantitative evaluation approach. However, our subsequent digital mental health support interventions have added post-intervention focus groups with research participants and other relevant knowledge users. |
| Comment 12: Clarify study limitations by discussing the absence of a control group, selection bias and small sample size limitations. |
| Response 12: See responses above. |
| Comment 13: Provide more detail on engagement and adherence to determine if increased platform interaction correlates with improved DSQoL outcomes. |
| Response 13: Engagement metrics for TRIFECTA are reported in a previously published journal article (Tang et al., 2024). The authors included a new subsection of the Results of the revised manuscript (“TRIFECTA Engagement Metrics & Associations with T1DAL Scales”) to address this reviewer’s comment and provide more information about engagement and associations with T1DAL outcomes. On page 4, lines 135-144, it reads: “3.3. TRIFECTA Engagement Metrics & Associations with T1DAL Scales. The descriptive statistics associated with engagement in the TRIFECTA peer support platforms were reported in a prior publication [6]. Descriptive statistics for each platform engagement metric were as follows: Huddle Sessions M=2, SD=2; Ask-the-expert Posts M=1, SD=2; Ask-the-expert Views M=19, SD=18; and WhatsApp Messages M=22, SD=36. Change in overall T1DAL score for 26-45-year-old’s was significantly associated with Ask-the-expert Views (ρ=0.574, B-H adj. p-value = 0.008) and with Huddle Sessions attended (ρ=0.515, B-H adj. p-value = 0.016). Counts of WhatsApp Messages and Ask-the-expert Posts were not associated with T1DAL change scores for this age band. No other associations for other age bands were found to be statistically significant.” |
| Comment 14: Enhance statistical reporting by justifying the choice of statistical tests and discussing statistical power concerns. |
| Response 14: The Statistical Analysis subsection of the Methods of the revised manuscript along with *note of table 2 has been updated to provide further justification for the choice of statistical tests. |
| Comment 15: Expand the discussion to include possible explanations for differential age group benefits and potential strategies to enhance engagement for older adults. |
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Response 15: As suggested on page 6-7, lines 177-189, we added the following text: “Consistent with our findings, among 193 children 10-18 years with T1D, Iafusco and colleagues [9], also reported elevations in overall DSQoL following a 2-year digital health intervention involving monthly synchronous chat line with peers and health professionals [9]. Although, when stratifying by age cohort, we found only the cohort with the largest number of participants (26-45 years) reported increases in DSQoL. This finding could be related to having more statistical power due to larger subsample size or due, in part, to differential engagement by age. In fact, the 26-45 year old cohort attended more Huddle sessions compared to their 18-25 year old counterparts. Interestingly, previous research also found higher utilization rates for digital health management interventions among “middle-aged adults” compared to “young adults” [10]. Subsequent research should examine a possible dose response relationship with DSQoL change across age bands with larger subsample sizes to verify this preliminary finding.” [10] Jeong, S.-H.; Nam, Y.G. The Paradox of Digital Health: Why Middle-Aged Adults Outperform Young Adults in Health Management Utilization via Technology. Healthcare 2024, 12, 2261. https://doi.org/10.3390/healthcare12222261 In addition, on page 7, lines 201-204, we added the following text: “Nonetheless, efforts to engage older adults than 60 years should be considered as research suggests that this age group is less likely to utilize digital health or social services [17]. Offering virtual orientations or instructional videos that demonstrate how to navigate the respective digital interventions may boost participation.” [17] Heponiemi T, Kaihlanen A-M, Kouvonen A, Leemann L, Taipale S, Gluschkoff K. The role of age and digital competence on the use of online health and social care services: A cross-sectional population-based survey. DIGITAL HEALTH 2022, 8. https://doi.org/10.1177/20552076221074485 |
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Comment 16: Address generalizability concerns by discussing demographic limitations and their implications for broader T1D populations. |
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Response 16: As suggested by the reviewer, on page 7, lines 222-225, we have added the following text: “Finally, because our sample was largely White and female, results cannot be generalized to the larger T1D population nor can results reflect non-lockdown conditions. Future studies should recruit a more sociodemographically diverse sample size across age bands.”
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Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsDear Authors,
the comments in the annex file.
Best
Comments for author File: Comments.pdf
Author Response
Comments 1: I thank the authors and the journal for seeking my advice regarding the article in question, but there is one element that hinders its progression: the type of dissemination chosen (brief report), which is not contemplated by the journal. Given the topic and the preliminary elements of interest regarding the data, I suggest the authors create a full Article with all its sections in order to promote the scientific dissemination it deserves. In its current form, it should be rejected, but it would be a real shame because the topic is relevant and the results promising. I remain available to the authors and the journal for a reassessment after the necessary transition to Article. I strongly encourage the authors to make the necessary effort to improve their proposal. Thank you for your trust and collaboration.
Response 1: We would like to refute the reviewers' comments. The reviewer requested to transform our 'brief report' into a full research report as this is not a submission category option in the Diabetology journal (which is not accurate - as this category was described in the author guidelines). In addition, the scope of our investigation fits with a brief report framework - not a full research report.
Reviewer 3 Report
Comments and Suggestions for AuthorsIn this study, authors analyzed how digital support intervetion helps patients improve their quality of life in Canada's type-1 diabetes patients. Although a small number of patients participated and there was a limitation in that the sample did not show an even distribution, it is a very good study that suggested the usefulness of digital tools in the treatment and management of diabetes in the future.
In order to increase the completeness of this study, please revise the following.
-The abstract of the paper is too short. In particular, please describe the motives and methods of the study.
-Who is the TRIFECTA used in this study fundamentally for? Is it a diabetic patient? Or can the general public interested in diabetes participate?
-What is the basis for dividing the survey subjects into four age groups: 18-25, 26-45, 46-60, and 60 or older in this study?
-88 lines: 35–45 => 26–45
-Most of the discussion parts described in Chapter 4 are consistent with the results of previous studies, and it is necessary to describe more clearly what is the greatest achievement of this study.
-There is no need to describe the study's limitations (209-220 lines) described in Chapter 4 because it was mainly caused by the small number of survey participants in this study.
-It is desirable to create a new chapter 5 and describe the conclusions separately.
Author Response
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Comments 1: The abstract of the paper is too short. In particular, please describe the motives and methods of the study. Response 1: As suggested by the reviewer, on page 1, the revised abstract reads: “Although digital platforms have gained popularity in the delivery of diabetes interventions, few models have focused on type 1 diabetes (T1D), offer different support delivery mechanisms, and involve peer and health professional-led support. TRIFECTA is a six-month multi-modal digital support intervention that includes a 24/7 peer texting group, an “ask-the-expert” web-based portal, and professional-led virtual group-based interactive sessions. This study examined diabetes-specific quality of life (DSQoL) changes following TRIFECTA. DSQoL was measured using Type 1 Diabetes and Life, a self-report survey that allows for subscale analysis in different age groups. Among 60 adults with type 1 diabetes, improvements were observed for overall diabetes-specific quality of life, primarily driven by the 26-45 years cohort. Subscale analysis found DSQoL improved for emotional experiences and daily activities for adults 26-45 years old, and social isolation improved for adults 46-60 years old.” |
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Comments 2: Who is the TRIFECTA used in this study fundamentally for? Is it a diabetic patient? Or can the general public interested in diabetes participate? Response 2: As suggested by the reviewer, we added the following on page 2-3, lines 71-73: “TRIFECTA was designed specifically for adults with T1D, as peer support is delivered by other adults with the same condition and self-management support is delivered by health professionals who specialize in T1D” |
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Comments 3: What is the basis for dividing the survey subjects into four age groups: 18-25, 26-45, 46-60, and 60 or older in this study? Response 3: Quality of life issues can be different across the life span. For example, in the development of the T1DAL DSQoL survey, peer relationships emerged as an especially relevant dimensions for younger adults 18-25 but not as critical for adults 60 and older. Alternatively, social isolation was a dimension identified by adults 46-60 and 60 and older, but not for 18-25 and 26-45 year olds. For this reason, the T1DAL DSQoL survey addresses the unique issues experienced across different age cohorts (18-25, 26-45, 46-60, and 60 or older). Although the results of overall T1DAL are important to this study, the subscale analysis allowed us to examine the unique impact of digital health interventions through different ages. On page 3, lines 105-107, we added the following: "In contrast to other DSQoL measures that pertain to all adults ≥18 years, the T1DAL survey addresses age-relevant T1D experiences across the lifespan. This age stratification allows for sub-group analysis.” |
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Comment 4: 88 lines: 35-45 => 26-45 Response 4: We appreciate the reviewer for catching this small mistake. The number has now been changed to its correct age group on page 3, line 100. |
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Comment 5: Most of the discussion parts described in Chapter 4 are consistent with the results of previous studies, and it is necessary to describe more clearly what is the greatest achievement of this study. Response 5: As suggested by the reviewer, we have added the following on page 7, lines 190-193: “To date, DSQoL surveys for adults have been developed and validated for all adults ≥18 rather than tailored to age-bound subgroups such as young adults, middle-aged adults, and older adults. This is one of the few studies that has utilized a DSQoL instrument (T1DAL) customized across the adult lifespan [9].” |
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Comment 6: There is no need to describe the study's limitations (209-220 lines) described in Chapter 4 because it was mainly caused by the small number of survey participants in this study. Response 6: In response to Reviewer 1’s request, we added greater details and more examples for the limitations section. If there is a problem in keeping the detailed limitations we can trim down this section. |
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Comment 7: It is desirable to create a new chapter 5 and describe the conclusions separately. Response 7: As suggested by the reviewer, a separate section ‘5. Conclusion’ has been created, on page 8, starting from line 248. |
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsThe authors have added comments to clarify various documents, explained more detailed methodological issues, and added additional references. For these reasons, I believe that their manuscripts have significantly improved.
Author Response
Comment 1: The authors have added comments to clarify various documents, explained more detailed methodological issues, and added additional references. For these reasons, I believe that their manuscripts have significantly improved.
Response 1: We appreciate your time and approval of our revised draft.
Reviewer 2 Report
Comments and Suggestions for AuthorsDear Authors,
missing the basic elements for costructive peer review process.
Best
Comments on the Quality of English LanguageMerit native review
Author Response
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Comments 1: missing the basic elements for costructive peer review process. Response 1: we thank the reviewer for taking the time to review this manuscript. |
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Response to Comments on the Quality of English Language: Point 1: Merit native review Response 1: Respectfully, we do not believe this manuscript requires review by a professional English-proficient copy editor. As noted in Reviewer 2's initial report, the quality of English in our manuscript was deemed satisfactory, as evidenced by the check on 'The English is fine and does not require any improvement. Dr. Tang, the senior author of this manuscript, has extensive academic and professional experience. Her expertise and proficiency in English are demonstrated by her publication of over 108 papers in peer-reviewed journals, none of which have required professional editing. As such, we do not feel a professional copy editor is warranted. |
