Type 1 Diabetes Mellitus (T1DM) is a common chronic disease of childhood. T1DM has substantial impact on quality of life (QOL), including burdensome dietary restrictions and the need to count carbohydrates in foods to safely dose insulin. Carbohydrate counting is challenging, inconvenient, and, if done wrong, can cause high or low blood glucose levels. To address these challenges, iSpy, a novel smartphone application, was created to identify foods and determine their carbohydrate content using pictures or speech. This pilot study is to evaluate if using iSpy improves carbohydrate counting accuracy and efficiency. Pilot participants will have carbohydrate counting (accuracy and efficiency) and their overall QoL (with respect to carbohydrate counting) assessed at baseline and after 3-months. The investigators hypothesize that using iSpy will make carbohydrate counting easier (by improving accuracy and efficiency) and enhance QoL for patients and/or their caregivers. If so, iSpy may help lessen the burden of living with T1DM.
Nutrition is an integral component of management of many chronic diseases and of overall wellness. Helping individuals to understand what they are eating can empower them to better manage their diseases. For example, the growing number of youth living with Type 1 Diabetes Mellitus (T1DM) struggle with carbohydrate counting, an essential and daily aspect of their lives, because of required reliance on memorization and numeracy skills. Effective carbohydrate counting has been demonstrated to improve blood glucose control, while inaccurate carbohydrate counting results in more variable blood glucose. Concerns related to carbohydrate counting accuracy can also limit food choices, provoke anxiety, and decrease quality of life. Since there is no cure for T1DM, enhancing patients' ability to understand and apply carbohydrate counting is an important part in helping them manage their condition most effectively. iSpy is a novel healthcare application that addresses an important clinical need by facilitating carbohydrate counting using pictures or voice recognition. Proprietary algorithms adjust for portion size and identify hidden carbohydrates (such as in ketchup or other condiments) and quantify the amount of carbohydrates in a meal.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
SUPPORTIVE_CARE
Masking
NONE
Enrollment
46
iSpy is a novel healthcare application that hopes to address an important clinical need by facilitating carbohydrate counting using pictures or voice recognition. Proprietary algorithms adjust for portion size and identify hidden carbohydrates (such as in ketchup or other condiments) and quantify the amount of carbohydrates in a meal.
The Hospital for Sick Children
Toronto, Ontario, Canada
Carbohydrate (CHO) Counting Accuracy
Assessing CHO counting using 10 food items from all major food groups. For each group, a simple food (e.g. an apple) and complex food (e.g. food with 2+ components but base food from targeted group) is included. A Co-investigator, Registered Dietitian (RD)/Certified Diabetes Educator (CDE), selected 2 sets of 10 food items for the study visits (verified that both sets were similar difficulty). The net CHO value (true value) for each food will be based on the nutrition label, USDA Nutrient Database (Release 28), Canadian Nutrient File, or by RD/CDE. For each food, data will be obtained from all participants: • Estimated net CHO (in grams) And with the above data, the following will be calculated: * % of food for which subjects estimated the CHO content within (+/-) 10 grams of true value. * Change (%) in the above accuracy measure for counting at baseline versus 3-month follow-up visit in iSpy vs control groups.
Time frame: 3 months
Carbohydrate (CHO) Counting Efficiency (Time to count)
Assessing CHO counting using 10 food items from all major food groups. For each group, a simple food (e.g. an apple) and complex food (e.g. food with 2+ components but base food from targeted group) is included. A Co-investigator, Registered Dietitian (RD)/Certified Diabetes Educator (CDE), selected 2 sets of 10 food items for the study visits (verified that both sets were similar difficulty). The net CHO value (true value) for each food will be based on the nutrition label, USDA Nutrient Database (Release 28), Canadian Nutrient File, or by RD/CDE. For each food, data will be obtained from all participants: • Time required to estimate net CHO (in seconds) In order to calculate the following: • Change in the above time taken for counting at baseline versus 3-month follow-up visit in iSpy vs control groups.
Time frame: 3 months
Change in Quality of Life: Self Care Inventory Questionnaire
Quality of Life questionnaires will be analyzed for any change from baseline to 3-month follow-up within both the intervention and control groups. The first questionnaire including: • Self Care Inventory: Scale from 1 (Never do it) to 5 (Always do this as recommended without fail);
Time frame: 3 months
Change in Quality of Life: Diabetes Questionnaire
Quality of Life questionnaires will be analyzed for any change from baseline to 3-month follow-up within both the intervention and control groups. The second questionnaire including: • Diabetes Family Responsibility Questionnaire: Selection of 1 of 3 statements best describing the way each task is handled in the family from 1 (Parent taking responsibility almost all of the time) 2 (Parent and Child sharing responsibility equally) to 3 (Child taking responsibility almost all of the time);
Time frame: 3 months
Change in Quality of Life: Quality of Life for Youth
Quality of Life questionnaires will be analyzed for any change from baseline to 3-month follow-up within both the intervention and control groups. The third questionnaire including: • Quality of Life for Youth: Scale from 0 (Never) to 4 (All the time);
Time frame: 3 months
Change in Quality of Life: Global Quality of Life
Quality of Life questionnaires will be analyzed for any change from baseline to 3-month follow-up within both the intervention and control groups. The last questionnaire including: • Global Quality of Life: Scale from 1 (No Change) to 7 (A great deal better);
Time frame: 3 months
Implementation outcomes: Accrual/Attrition Rates
• Criteria for implementation success will be based on the studies previously conducted by Co-Principal Investigator (JS): Participant accrual (total completed / total enrolled) and attrition rates (total dropouts / total enrolled). Success being defined as accrual rates \>70% and attrition rates \<15%;
Time frame: 3 months
Implementation outcomes: Engagement
• Levels of engagement will be assessed as follows: * Low: defined as less than 1 logged meal every 2 weeks * High: defined as greater than 1 logged meal every 2 weeks.
Time frame: 3 months
Implementation outcomes: Acceptability
• Acceptability: 7 item Acceptability e-Scale (AES) regarding level of acceptability from a scale of 1 (low) to 5 (high) of iSpy, with high acceptability being item mean score of 4 on AES. 2 additional qualitative items are included in AES and will be evaluated.
Time frame: 3 months
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