Technological-based Personalized Care Intervention for Supporting Older People With Diabetes Mellitus
Phase: NA
Conditions: Diabetes Mellitus
Sponsor: Hong Kong Metropolitan University

Document updated date: 12/09/2023 
 
Cover letter: 
 
Official project title:  
Technological-based Personalized Care Intervention for Supporting Older People with Diabetes 
Mellitus 
Funding source: HKNU R&D Fund 
 
NCT number: HE-RD/2023/1.11 
 
Document updated date: 12/09/2023 
 

Document updated date: 12/09/2023 
 
                 HONG KONG METROPOLITAN UNIVERSITY  
 
                                           Study protocol 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 1: A flow diagram of the study 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
An invitation poster will be displayed in the centers or on the web page of the centers 
and then information sessions will be held in the centers two months before 
Participants fitting the inclusion criteria will be selected by convenience 
sampling  
Arm 1: 
Non-invasive blood glucose 
monitoring  (n= 30) 
Arm 2: 
Traditional self-
monitoring (n= 30) 
Sensor set up (1st to 2nd 
week and 7th to 8th week) 
Self-monitoring of Blood 
Glucose (SMBG) 
 (3rd to 6th week)  
8 weeks of monitor DM 
control via Self- 
Monitoring of Blood 
Glucose (SMBG) 
 
The outcome measurements will be recorded before the intervention (T0), and 
immediately after the 8-week intervention (T1)  
Eligible participants will be randomly assigned in a 1:1 ratio 
Participants will be recruited from local community elderly center 

Document updated date: 12/09/2023 
 
 
1. Research methodology 
 
Diabetes mellitus (DM) has been declared a growing public health threat. DM affects 
approximately 463 million people around the world (1). Regarding the World Health 
Organization (WHO), DM is defined as fasting glucose equal to or higher than 7.0 mmol/L or 
a glucose level equal to or higher than 11.1 mmol/L two hours after a meal. A1C reflects the 
average blood glucose control in the last 2 to 3 months. As stated by American Diabetes 
Association (ADA), the optimal A1C level should be less than 7%. The prediabetes stage 
defines as an A1C level between 5.7 and less than 6.5%. WHO calls upon to strengthen, 
share and disseminate standards and to support interventions for diabetes management, 
preventing and slowing the progression of complications of DM through population and 
individual approaches (2). DM affects about 8.4% of the total population in Hong Kong 
(1). DM virtually affects the physiology of every body system mainly due to metabolic 
disturbances caused by hyperglycemia (2, 3).  
 
Individuals with diabetes can make a dramatic impact on the disease progression  
and complications via self-management, which includes activities and behaviors of an 
individual undertakes to control and manage his or her health conditions. Poor diabetes 
management frequently causes more issues, which ultimately result in prolonged hospital 
admissions, more frequent outpatient visits, and higher prescription expenses.   
 
Common self-management behaviors for individuals with diabetes are blood sugar 
monitoring, adopting healthy eating, being physically active, compliance with medications 
and healthy coping skills with good knowledge. Participating in all these five care has been 
demonstrated to have a significant effect on the progression and development of diabetes 
for individuals, as all of these five factors are associated with improved glycemic control, 
reduced complications and enhanced quality of life.  
 
Just-in-time adaptive intervention (JITAI) is an evidence-based intervention to help 
intervention designers design to provide behavior change at right type/amount of support 
needed at right time at individual level (4). Mobile technologies such as smart-phones, 
mobile apps and various sensors can be used to provide interventions whenever and 
wherever needed (5).  The core component of adaptive intervention embraces decision 
points (point in time at which treatment decisions made), intervention options (types, 
sources and modes of support delivery), tailoring variables (information concerning the 
participant that is used to make treatment decisions) and decision rules (which intervention 
option to offer based on the tailoring variables) (6). JITAI facilitates intervention designers to 
offer support in a timely manner and help the participant maintaining healthy behaviors. JITAI 
have been used to develop different interventions including physical activity and dietary 
counselling (5), mental health (7), and harmful substances (8). In recent study, the authors 
used JITAI to improve physical activity in older adults with type II DM via a mobile application 
(9).  
 
1. 
Saeedi, P., Petersohn, I., Salpea, P., Malanda, B., Karuranga, S., Unwin, N., Colagiuri, S., 
Guariguata, L., Motala, A. A., Ogurtsova, K., Shaw, J. E., Bright, D., & Williams, R. (2019). 
Global and regional diabetes prevalence estimates for 2019 and projections for 2030 

Document updated date: 12/09/2023 
 
and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. 
Diabetes Research and Clinical Practice, 157, 107843.  
2. 
World Health Organization. (2016). Global Report on Diabetes. In World Health 
Organization (Vol. 978). https://doi.org/ISBN 978 92 4 156525 7 
3. 
Centre for Health Protection. (2017). Report of Population Health Survey 2014/2015. 
4. 
Wang, L., & Miller, L. C. (2020). Just-in-the-Moment Adaptive Interventions (JITAI): A 
Meta-Analytical Review. Health Communication, 35(12), 1531–1544. 
https://doi.org/10.1080/10410236.2019.1652388 
5. 
Goldstein, S. P., Zhang, F., Klasnja, P., Hoover, A., Wing, R. R., & Thomas, J. G. (2021). 
Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in 
Behavioral Obesity Treatment: Protocol for a Microrandomized Trial. JMIR Research 
Protocols, 10(12). https://doi.org/10.2196/33568 
6. 
Hardeman, W., Houghton, J., Lane, K., Jones, A., & Naughton, F. (2019). A systematic 
review of just-in-time adaptive interventions (JITAIs) to promote physical activity. 
International Journal of Behavioral Nutrition and Physical Activity, 16(1). 
7. 
Teepe, G. W., da Fonseca, A., Kleim, B., Jacobson, N. C., Sanabria, A. S., Car, L. T., 
Fleisch, E., & Kowatsch, T. (2021). Just-in-time adaptive mechanisms of popular mobile 
apps for individuals with depression: Systematic app search and literature review. 
Journal of Medical Internet Research, 23(9).  
8. 
Perski, O., Hébert, E. T., Naughton, F., Hekler, E. B., Brown, J., & Businelle, M. S. (2022). 
Technology-mediated just-in-time adaptive interventions (JITAIs) to reduce harmful 
substance use: a systematic review. Addiction, 117(5), 1220–1241. 
9. 
Pimenta, N., Félix, I. B., Monteiro, D., Marques, M. M., & Guerreiro, M. P. (2022). 
Promoting Physical Activity in Older Adults With Type 2 Diabetes via an 
Anthropomorphic Conversational Agent: Development of an Evidence and Theory-
Based Multi-Behavior Intervention. Frontiers in Psychology, 13(July).  
 
 
Setting and Participants: Convenience sampling will be used to select participants who 
meet the inclusion criteria for a two-arm randomized controlled trial (RCT). A block 
randomization approach will be utilized to allocate participants into either the intervention 
or control 
group. 
This 
will 
be 
accomplished 
using 
a computer 
program that 
generates random block sizes of 2 or 4 in a 1:1 ratio. The study will be conducted as a single-
blind trial, with assessors being the only ones blinded to treatment allocation. Community 
centers will display promotional flyers to recruit potential participants, who will then 
undergo a screening process. The participants will provide written informed consent before 
the study starts. We propose to conduct an RCT with two treatment arms to clarify the 
effects of non-invasive blood glucose device on blood glucose monitoring. The two arms will 
be the non-invasive blood glucose monitoring (arm 1) and traditional self-monitoring (arm 2) 
as a control. The outcome measurements will be recorded before the intervention (T0) and 
immediately after the 8-week intervention (T1).  
 
        Project plan and methodology  
Quantitative arm – Setting and Participants: A block randomization, single-blind design will 
be used. A two-arm RCT with equal allocation between the intervention and control groups 
(1:1) will be used; only the assessors will be blinded to the treatment assignment. 
Convenience sampling with fitting the inclusion criteria will be selected as subjects. 

Document updated date: 12/09/2023 
 
Potential participants will be screened for fasting blood glucose and health history at 
baseline assessment. The potential participants who meet the inclusion criteria will be 
asked to sign a consent form before participation in this study.  
 
Inclusion and exclusion criteria 
A total of 60 those who are aged 60 and above and meet the inclusion criteria will be asked 
to sign a consent form before participation in this study. The inclusion criteria are: (i) aged 
≥ 60 years old; (ii) patients diagnosed with Type II DM (iii) patients experienced hypo (histix 
< 4.0 mmol/L) or hyperglycemia (histix ≥16.0 mmol/L); & (iV) The Abbreviated Mental Test 
(AMT) ≥ 6. The exclusion criteria are: (i) patient on dialysis; (ii) taking ascorbic acid > 500mg 
daily or (Not recommended by manufacturer as result may be affected); (iii) taking salicylic 
acid ≥ 650mg single dose or (Not recommended by manufacturer as result may be affected); 
or (iV) scheduled radiological therapy during CGM application (Not recommended by 
manufacturer as result may be affected).   
 
Primary and secondary outcomes 
The primary outcomes will be the point of care test (POCT) of HbA1C level and the secondary 
outcomes will be the fasting blood glucose, total cholesterol, blood pressure, 
anthropometric measurement, and Diabetes Self-Care activities questionnaire.  
 
HbA1C level: The HbA1C will be measured by A1CNow system, which has been cleared by 
the Food and Drug Administration (FDA) and certified by National Glycohemoglobin 
Standardization Program.  
 
Lipid and glucose panel: The POCT lipid panel includes total cholesterol and blood glucose 
will be measured by CardioChek, which has been cleared by the Food and Drug 
Administration (FDA) and certified by the Centers for Disease Control and Prevention, 
Cholesterol Reference Method Laboratory Network (CRMLN). 
 
Basic anthropometric measurement: The basic anthropometric measurement includes 
height, weight and body mass index.  
 
Blood pressure: blood pressure will be measured by blood pressure monitors (Omron M7, 
Intelli IT, Kyoto, Japan) for assessment of brachial blood pressure.  
 
Breath and blood Ketone: breath and blood ketone will be used to detect the presence of 
ketones in the breath and blood as indicators of hyperglycemia or diabetic ketoacidosis in 
individuals with diabetes.  
 
Summary of Diabetes Self-Care Activities (SDSCA) Chinese version: To assess participants’ 
diabetes self-care activities. The SDSCA will generate scores for five subscales: i) general 
diet, ii) eating a diet high in fruits/vegetables and low in high-fat foods, iii) daily activity, non-
leisure and leisure activity, using three categories namely ‘most active’, ‘moderately active’ 
and ‘least active’, iv) taking recommended medications, v) glucose self-monitoring. Scores 
will be calculated on a continuous scale from 0-7, with the numerical value for each item 
based on the number of days during the week that the behavior was practiced. The item 
scores will then be averaged to derive an overall score for each self-care activity. The internal 

Document updated date: 12/09/2023 
 
reliability of the SDSCA is robust, acceptable reliability (0.55–0.64), with Cronbach's alphas 
0.7.  
 
Demographic and other covariates 
Apart from gathering socio-demographic information such as age, marital status, gender, 
family income and education attainment, we will obtain data from the participants regarding 
their physical activity level, alcohol consumption, self-reported medical diagnosis, current 
medication usage and family history of first-degree relatives.   
 
Statistical analysis 
The statistical analysis will be performed using SPSS (Version: 26, SPSS Inc, Chicago). The 
alpha level will be set at 0.05 for two-tailed tests. One way analysis of variance 
model (ANOVA) will be used to compare the post-treatment scores over two time points.  
 
Interventions: Non-invasive blood glucose monitoring  
Non-invasive wearable glucose monitoring device will be used to capture participants’ 
blood glucose. This device is non-invasive with no consumables, which used to check for 
participants’ compliance with blood glucose monitoring and trend of blood glucose.  
 
The arrangement of the intervention will be (1) training for self-management of DM and 
phone consultation; (2) phone call (or WhatsApp) follow up of hypo-or hyper-glycemic 
situations; (3) using sensor data to guide personalized care intervention. We will monitor 
the health data collected by this device regularly. A mobile application will be uploaded and 
recorded to monitor their compliance with the blood glucose monitoring.  
 
Control group: Traditional self-monitoring of blood glucose 
A control group with traditional self-monitoring of blood glucose will be used. The 
arrangement of the control group will be (1) training for self-management of DM and phone 
consultation; (2) phone call (or WhatsApp) follow up; (3) traditional self-monitoring of blood 
glucose will be used to guide personalized care intervention. The control group will be used 
as a comparative group to assess the effect of the non-invasive device in blood glucose 
monitoring.  
 
Trained research assistant will keep close contact with all 60 participants through the 
scheduled regular phone calls or WhatsApp messages to understand their compliance and 
commitment to the monitoring of blood glucose. Analysis will be done to evaluate the 
effectiveness of the non-invasive wearable blood glucose monitoring device.