James E. Aikens

Background: Type 1 diabetes (T1D) requires repeated self-management behaviors and ongoing problem-solving to maintain optimal glucose levels and prevent complications. Despite increasing adoption of continuous glucose monitoring (CGM), which can alleviate some of the constant self-management burden, adolescents struggle to achieve glycemic recommendations and report low engagement with diabetes device data. Previous studies have used retrospective or quantitative approaches to describe adolescent self-management; however, it is unclear how psychosocial influences (eg, mood and distress) and contexts impact adolescent self-management behaviors and engagement with their diabetes devices in everyday life. Exploration of real-time experiences will help to identify potential targets and strategies for future interventions to improve glycemic outcomes in adolescents with T1D using advanced diabetes technologies. Objective: This study has two aims: (1) to develop a grounded theory of self-management decision-making using diabetes devices among adolescents with T1D and (2) to assess the acceptability and feasibility of longitudinal and real-time qualitative data collection methods in this population. Methods: We will conduct a mixed methods study informed by the capability, opportunities, and motivation of behavior model. Adolescents (aged 12‐18 y) with T1D who regularly use CGMs will be recruited from a Midwest pediatric diabetes clinic. Purposive sampling strategy will ensure participants with varied glycemic levels (hemoglobin A _1c [HbA _1c ] ≤9% and HbA _1c >9%) and diabetes experiences (eg, diabetes duration, devices used) are included. Using a longitudinal convergent mixed methods design, enrolled participants (n=30‐40) will complete data collection over 6 weeks including: (1) a baseline survey to capture demographic, clinical, and behavioral characteristics; (2) 30 days of SMS text messaging surveys to describe real-time self-management behaviors, technology use, and decision-making; (3) 30 days of CGM data; and (4) an interview focused on self-management behaviors and technology use. Recruitment will continue until appropriate data completeness and/or theoretical saturation is achieved. Analysis of text responses and interview transcripts will follow a grounded theory approach. Summarized glycemic metrics (eg, time in range) and visuals (ie, ambulatory glucose profile) will be integrated with qualitative findings through participant profiles and joint displays. Integrated findings will be used to refine a grounded theory of daily self-management decision-making using diabetes devices among adolescents with T1D. Results: As of December 2025, 25 participants have enrolled in this study. We expect SMS text messaging survey completion rates and CGM use near 70% throughout the study period. We anticipate findings to become available in the following several years through conference presentations and peer-reviewed publications. Conclusions: While routine diabetes self-management behaviors and use of diabetes technologies are important for achieving glycemic goals, adolescents report low adherence to diabetes devices. This real-time mixed methods study will improve our understanding of daily decision-making and influences on diabetes self-management. Findings from this study will identify facilitators and barriers to optimal T1D self-management. In addition, results will inform future studies using real-time qualitative and mixed methods approaches.

Background: Choosing contraception is a highly personal, often complex decision. People with acute and chronic health conditions (eg, pulmonary embolism, diabetes, and hypertension) must deliberate additional factors including whether and to what extent a contraceptive method may affect their health conditions or interact with their medications. “My Health My Choice” (web application developed by Alfa Jango) is a web-based decision support tool designed to help patients understand their contraceptive options considering their health conditions, medications, and personal priorities. Objective: We will evaluate the efficacy of “My Health My Choice” (MHMC) use among people with diverse health conditions who seek contraception in US outpatient clinics. Methods: This mixed methods cluster randomized controlled trial will compare MHMC and usual contraceptive care (intervention arm) to usual contraceptive care only (control arm). Fourteen clinics that specialize in primary care or obstetrics and gynecology will be enrolled, with 7 clinics in each arm. We will enroll 726 patients (363 in each group) who (1) are 18-49 years old, (2) are able to speak and read English, (3) are seeking contraception for pregnancy prevention, and (4) have at least 1 health condition among 60 eligible health conditions and characteristics (eg, cigarette smoking and postpartum state). Aim 1 (quantitative) is to assess the primary outcome of patient-reported contraceptive nonuse (yes or no) at 3 months. Aim 2 (mixed methods) starts with a quantitative summary of patient-reported contraceptive use by method type (eg, pills and shot) and corresponding Medical Eligibility Risk Category (Category 1, 2, 3, or 4) based on the Centers for Disease Control and Prevention (CDC) US Medical Eligibility Criteria (US MEC) Contraceptive Guidelines. We will conduct exit interviews of a subsample of approximately 30 patients and 30 clinicians to qualitatively understand how MHMC and other contextual factors influenced perceptions about contraceptive risk and contraceptive decisions. Aim 3 (quantitative) is to explore patient-reported contraceptive decisional conflict (measured by the Decisional Conflict Scale) and patient-reported quality of interaction with their contraceptive provider (measured by the Interpersonal Quality of Family Planning Scale) as mediators of the effects of MHMC on the primary outcome. This paper describes the study protocol per the SPIRIT (Standard Protocol Items: Recommendations for Intervention Trials) checklist. Results: The National Institute of Child Health and Human Development funded this study (R01HD110570, Principal Investigator JPW) in 2023. The study team is collaborating with the DARTNet Institute (Aurora, Colorado) to enroll and prepare health clinics for trial launch. Fourteen clinics have enrolled in the trial and are in various stages of study preparation and regulatory approval. We anticipate patient enrollment to run from October 2025 to September 2028. Conclusions: This is the first mixed methods cluster randomized controlled trial of MHMC, a novel web-based decision support tool for people with health conditions. Trial Registration: ClinicalTrials.gov NCT07075536; https://clinicaltrials.gov/study/NCT07075536 International Registered Report Identifier (IRRID): DERR1-10.2196/71101

BACKGROUND Choosing contraception is a highly personal, often complex decision. People with acute and chronic health conditions (eg, pulmonary embolism, diabetes, and hypertension) must deliberate additional factors including whether and to what extent a contraceptive method may affect their health conditions or interact with their medications. “My Health My Choice” (web application developed by Alfa Jango) is a web-based decision support tool designed to help patients understand their contraceptive options considering their health conditions, medications, and personal priorities. OBJECTIVE We will evaluate the efficacy of “My Health My Choice” (MHMC) use among people with diverse health conditions who seek contraception in US outpatient clinics. METHODS This mixed methods cluster randomized controlled trial will compare MHMC and usual contraceptive care (intervention arm) to usual contraceptive care only (control arm). Fourteen clinics that specialize in primary care or obstetrics and gynecology will be enrolled, with 7 clinics in each arm. We will enroll 726 patients (363 in each group) who (1) are 18-49 years old, (2) are able to speak and read English, (3) are seeking contraception for pregnancy prevention, and (4) have at least 1 health condition among 60 eligible health conditions and characteristics (eg, cigarette smoking and postpartum state). Aim 1 (quantitative) is to assess the primary outcome of patient-reported contraceptive nonuse (yes or no) at 3 months. Aim 2 (mixed methods) starts with a quantitative summary of patient-reported contraceptive use by method type (eg, pills and shot) and corresponding Medical Eligibility Risk Category (Category 1, 2, 3, or 4) based on the Centers for Disease Control and Prevention (CDC) US Medical Eligibility Criteria (US MEC) Contraceptive Guidelines. We will conduct exit interviews of a subsample of approximately 30 patients and 30 clinicians to qualitatively understand how MHMC and other contextual factors influenced perceptions about contraceptive risk and contraceptive decisions. Aim 3 (quantitative) is to explore patient-reported contraceptive decisional conflict (measured by the Decisional Conflict Scale) and patient-reported quality of interaction with their contraceptive provider (measured by the Interpersonal Quality of Family Planning Scale) as mediators of the effects of MHMC on the primary outcome. This paper describes the study protocol per the SPIRIT (Standard Protocol Items: Recommendations for Intervention Trials) checklist. RESULTS The National Institute of Child Health and Human Development funded this study (R01HD110570, Principal Investigator JPW) in 2023. The study team is collaborating with the DARTNet Institute (Aurora, Colorado) to enroll and prepare health clinics for trial launch. Fourteen clinics have enrolled in the trial and are in various stages of study preparation and regulatory approval. We anticipate patient enrollment to run from October 2025 to September 2028. CONCLUSIONS This is the first mixed methods cluster randomized controlled trial of MHMC, a novel web-based decision support tool for people with health conditions. CLINICALTRIAL ClinicalTrials.gov NCT07075536; https://clinicaltrials.gov/study/NCT07075536 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/71101

Background: Hypoglycemia remains a challenge for roughly 25% of people with type 1 diabetes (T1D) despite using advanced technologies such as continuous glucose monitors (CGMs) or automated insulin delivery systems. Factors impacting hypoglycemia self-management behaviors (including reduced ability to detect hypoglycemia symptoms and unhelpful hypoglycemia beliefs) can lead to hypoglycemia development in people with T1D who use advanced diabetes technology. Objective: This study aims to develop a scalable, personalized mobile health (mHealth) behavioral intervention program to improve hypoglycemia self-management and ultimately reduce hypoglycemia in people with T1D who use advanced diabetes technology. Methods: We (a multidisciplinary team, including clinical and health psychologists, diabetes care and education specialists, endocrinologists, mHealth interventionists and computer engineers, qualitative researchers, and patient partners) jointly developed an mHealth text messaging hypoglycemia behavioral intervention program based on user-centered design principles. The following five iterative steps were taken: (1) conceptualization of hypoglycemia self-management processes and relevant interventions; (2) identification of text message themes and message content development; (3) message revision; (4) patient partner assessments for message readability, language acceptability, and trustworthiness; and (5) message finalization and integration with a CGM data–connected mHealth SMS text message delivery platform. An mHealth web-based SMS text message delivery platform that communicates with a CGM glucose information-sharing platform was also developed. Results: The mHealth SMS text messaging hypoglycemia behavioral intervention program HypoPals, directed by patients’ own CGM data, delivers personalized intervention messages to (1) improve hypoglycemia symptom detection and (2) elicit self-reflection, provide fact-based education, and suggest practical health behaviors to address unhelpful hypoglycemia beliefs and promote hypoglycemia self-management. The program is designed to message patients up to 4 times per day over a 10-week period. Conclusions: A rigorous conceptual framework, a multidisciplinary team (including patient partners), and behavior change techniques were incorporated to create a scalable, personalized mHealth SMS text messaging behavioral intervention. This program was systematically developed to improve hypoglycemia self-management in advanced diabetes technology users with T1D. A clinical trial is needed to evaluate the program’s efficacy for future clinical implementation.

Aims: Type 2 diabetes self-management occurs within social contexts. We sought to test the effects of Family/friends Activation to Motivate Self-care (FAMS), a self-care support intervention delivered via mobile phones, on psychosocial outcomes for persons with diabetes (PWDs) and their support persons. Methods: PWDs had the option to enroll with a friend/family member as a support person in a 15-month RCT to evaluate FAMS versus enhanced usual care. FAMS included 9-months of monthly phone coaching and text message support for PWDs, and text message support for enrolled support persons. Results: PWDs (N=329) were 52% male and 39% from minoritized racial or ethnic groups; 50% enrolled with elevated diabetes distress. Support persons (N=294) were 26% male and 33% minoritized racial or ethnic groups. FAMS improved PWDs’ diabetes distress ( d =-0.19) and global well-being ( d =0.21) during the intervention, with patterns of larger effects among minoritized groups. Post-intervention and sustained (15-month) improvements were driven by changes in PWDs’ self-efficacy, self-care behaviors, and autonomy support. Among support persons, FAMS improved helpful involvement without increasing burden or harmful involvement. Conclusions: FAMS improved PWDs’ psychosocial well-being, with post-intervention and sustained improvements driven by improved self-efficacy, self-care, and autonomy support. Support persons increased helpful involvement without adverse effects.

Aims: Family/friends Activation to Motivate Self-care (FAMS) is a self-care support intervention delivered via mobile phones. We evaluated FAMS effects on hemoglobin A1c (HbA1c) and intervention targets among adults with type 2 diabetes in a 15-month RCT. Methods: Persons with diabetes (PWDs) and their support persons (family/friend, optional) were randomized to FAMS or control. FAMS included monthly phone coaching and text messages for PWDs, and text messages for support persons over a 9-month intervention period. Results: PWDs (N=329) were 52% male, 39% from minoritized racial or ethnic groups, with mean HbA1c 8.6±1.7%. FAMS improved HbA1c among PWDs with a non-cohabitating support person (−0.64%; 95% CI [-1.22%, −0.05%]), but overall effects were not significant. FAMS improved intervention targets including self-efficacy, dietary behavior, and family/friend involvement during the intervention period; these improvements mediated post-intervention HbA1c improvements (total indirect effect −0.27%; 95% CI [-0.49%, −0.09%]) and sustained HbA1c improvements at 12 months (total indirect effect −0.19%; 95% CI [−0.40%, −0.01%]). Conclusions: Despite improvements in most intervention targets, HbA1c improved only among PWDs engaging non-cohabitating support persons suggesting future family interventions should emphasize inclusion of these relationships. Future work should also seek to identify intervention targets that mediate improvements in HbA1c.

BACKGROUND Hypoglycemia remains a challenge for roughly 25% of people with type 1 diabetes (T1D) despite using advanced technologies such as continuous glucose monitors (CGMs) or automated insulin delivery systems. Factors impacting hypoglycemia self-management behaviors (including reduced ability to detect hypoglycemia symptoms and unhelpful hypoglycemia beliefs) can lead to hypoglycemia development in people with T1D who use advanced diabetes technology. OBJECTIVE This study aims to develop a scalable, personalized mobile health (mHealth) behavioral intervention program to improve hypoglycemia self-management and ultimately reduce hypoglycemia in people with T1D who use advanced diabetes technology. METHODS We (a multidisciplinary team, including clinical and health psychologists, diabetes care and education specialists, endocrinologists, mHealth interventionists and computer engineers, qualitative researchers, and patient partners) jointly developed an mHealth text messaging hypoglycemia behavioral intervention program based on user-centered design principles. The following five iterative steps were taken: (1) conceptualization of hypoglycemia self-management processes and relevant interventions; (2) identification of text message themes and message content development; (3) message revision; (4) patient partner assessments for message readability, language acceptability, and trustworthiness; and (5) message finalization and integration with a CGM data–connected mHealth SMS text message delivery platform. An mHealth web-based SMS text message delivery platform that communicates with a CGM glucose information-sharing platform was also developed. RESULTS The mHealth SMS text messaging hypoglycemia behavioral intervention program HypoPals, directed by patients’ own CGM data, delivers personalized intervention messages to (1) improve hypoglycemia symptom detection and (2) elicit self-reflection, provide fact-based education, and suggest practical health behaviors to address unhelpful hypoglycemia beliefs and promote hypoglycemia self-management. The program is designed to message patients up to 4 times per day over a 10-week period. CONCLUSIONS A rigorous conceptual framework, a multidisciplinary team (including patient partners), and behavior change techniques were incorporated to create a scalable, personalized mHealth SMS text messaging behavioral intervention. This program was systematically developed to improve hypoglycemia self-management in advanced diabetes technology users with T1D. A clinical trial is needed to evaluate the program’s efficacy for future clinical implementation.

Background: A very low–carbohydrate (VLC) nutritional strategy may improve glycemic control and weight loss in adults with type 2 diabetes (T2D). However, the supplementary behavioral strategies that might be able to improve outcomes using this nutritional strategy are uncertain. Objective: This study aims to compare the impact of adding 3 different supplementary behavioral strategies to a web-based VLC diet intervention. To our knowledge, this is the first trial to randomize participants to different frequencies of dietary self-monitoring. Methods: The study included 112 overweight adults with T2D (hemoglobin A1c ≥6.5%) taking no antiglycemic medications or only metformin. They received a remotely delivered 12-month VLC diet intervention. Participants were randomly assigned through a full factorial 2×2×2 design to supplementary strategies: either daily or monthly dietary self-monitoring, either mindful eating training or not, and either positive affect skills training or not. Our research goal was to determine whether 3 different supplemental strategies had at least a medium effect size (Cohen d=0.5). Results: Overall, the VLC intervention led to statistically significant improvements in glycemic control (−0.70%, 95% CI −1.04% to −0.35%; P<.001), weight loss (−6.82%, 95% CI −8.57% to −5.08%; P<.001), and depressive symptom severity (Cohen d −0.67, 95% CI −0.92 to −0.41; P<.001). Furthermore, 30% (25/83) of the participants taking metformin at baseline reduced or discontinued their metformin. Only 1 Cohen d point estimate reached 0.5; daily (vs monthly) dietary self-monitoring had a worse impact on depressive symptoms severity (Cohen d=0.47, 95% CI −0.02 to 0.95; P=.06). None of the strategies had a statistically significant effect on outcomes. For changes in our primary outcome, hemoglobin A1c, the daily (vs monthly) dietary self-monitoring impact was 0.42% (95% CI −0.28% to 1.12%); for mindful eating, it was −0.47% (95% CI −1.15% to 0.22%); and for positive affect, it was 0.12% (95% CI −0.57% to 0.82%). Other results for daily (vs monthly) dietary self-monitoring were mixed, suggesting an increase in weight (0.98%) and depressive symptoms (Cohen d=0.47), less intervention satisfaction (Cohen d=−0.20), more sessions viewed (3.02), and greater dietary adherence (Cohen d=0.24). For mindful eating, the results suggested a benefit for dietary adherence (Cohen d=0.24) and intervention satisfaction (Cohen d=0.30). For positive affect, the results suggested a benefit for depressive symptoms (Cohen d=−0.32), the number of sessions viewed (3.68), dietary adherence (Cohen d=0.16), and intervention satisfaction (Cohen d=0.25). Conclusions: Overall, our results support the use of a VLC diet intervention in adults with T2D. The addition of monthly (not daily) dietary self-monitoring, mindful eating, and positive affect skills training did not show a definitive benefit, but it is worth further testing. Trial Registration: ClinicalTrials.gov NCT03037528; https://clinicaltrials.gov/ct2/show/NCT03037528

Background: Identifying caregiver availability, particularly for patients with dementia or those with a disability, is critical to informing the appropriate care planning by the health systems, hospitals, and providers. This information is not readily available, and there is a paucity of pragmatic approaches to automatically identifying caregiver availability and type. Objective: Our main objective was to use medical notes to assess caregiver availability and type for hospitalized patients with dementia. Our second objective was to identify whether the patient lived at home or resided at an institution. Methods: In this retrospective cohort study, we used 2016-2019 telephone-encounter medical notes from a single institution to develop a rule-based natural language processing (NLP) algorithm to identify the patient’s caregiver availability and place of residence. Using note-level data, we compared the results of the NLP algorithm with human-conducted chart abstraction for both training (749/976, 77%) and test sets (227/976, 23%) for a total of 223 adults aged 65 years and older diagnosed with dementia. Our outcomes included determining whether the patients (1) reside at home or in an institution, (2) have a formal caregiver, and (3) have an informal caregiver. Results: Test set results indicated that our NLP algorithm had high level of accuracy and reliability for identifying whether patients had an informal caregiver (F1=0.94, accuracy=0.95, sensitivity=0.97, and specificity=0.93), but was relatively less able to identify whether the patient lived at an institution (F1=0.64, accuracy=0.90, sensitivity=0.51, and specificity=0.98). The most common explanations for NLP misclassifications across all categories were (1) incomplete or misspelled facility names; (2) past, uncertain, or undecided status; (3) uncommon abbreviations; and (4) irregular use of templates. Conclusions: This innovative work was the first to use medical notes to pragmatically determine caregiver availability. Our NLP algorithm identified whether hospitalized patients with dementia have a formal or informal caregiver and, to a lesser extent, whether they lived at home or in an institutional setting. There is merit in using NLP to identify caregivers. This study serves as a proof of concept. Future work can use other approaches and further identify caregivers and the extent of their availability.