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Article: A mobile app for identifying individuals with undiagnosed diabetes and prediabetes (UDPD) and changing behavior: A two-year prospective study

TitleA mobile app for identifying individuals with undiagnosed diabetes and prediabetes (UDPD) and changing behavior: A two-year prospective study
Authors
Issue Date2018
PublisherJMIR Publications, Inc. The Journal's web site is located at http://mhealth.jmir.org/
Citation
JMIR mHealth and uHealth, 2018, v. 6 n. 5, p. e10662:1-e10662:10 How to Cite?
AbstractBackground: To decrease the burden of diabetes in society, early screening of undiagnosed diabetes and prediabetes (UDPD) is needed. Integrating a diabetes risk score into a mobile app provides a useful platform to enable people to self-assess the risk of diabetes with ease. Objective: The objectives of this study were to: (1) assess the profile of app users of the Diabetes Risk Score (DRS) mobile app, (2) determine the optimal cut-off value of the Finnish diabetes risk score (FINDRISC) to identify UDPD in the Chinese population, (3) estimate the chance of developing diabetes within 2 years of using the app, and (4) investigate high-risk app users’ lifestyle behavior change after ascertaining their risk level from the app. Methods: The study was divided into two phases. Phase 1 adopted a cross-sectional design. A descriptive analysis was performed on the app users’ profile. Cohen’s Kappa score was used to show the agreement between the risk level (as shown in the app) and HbA1c test results. Sensitivity, specificity, and area under curve were used to determine the optimal cut-off value of the DRS in this population. Phase 2 was a prospective cohort study, using an online survey to follow up the app users. Logistic regression model was used to estimate the chance of developing diabetes within 2 years after using the app. Paired t-tests were used to compare the high-risk app users’ lifestyle change. Results: A total of 13,289 people used the app from 28 August 2014 to 31 December 2016. After data cleaning, 4,549 of these were considered as valid data. The majority of users were males, and about 40% had tertiary education or above. The optimal value of the DRS for identifying persons with UDPD was recommended to be 9, with an area under the ROC curve of 0.67 (P < .01, 95% CI: 0.60-0.74), sensitivity of 0.70 (95% CI: 0.58-0.80), and specificity of 0.57 (95% CI: 0.47-0.66). At the 2-year follow up, people in the high-risk group had a higher chance of developing diabetes (OR 4.59, 95% CI: 1.01 to 20.81, P = .048) than the low-risk group. The high-risk app users improved their daily intake of vegetables (Baseline: M = 0.76, SD = 0.43; follow-up: M = 0.93, SD = 0.26; t (81) = -3.77, P < .001) and daily exercise (Baseline: M = 0.40, SD = 0.49; follow-up: M = 0.54, SD = 0.50; t(81) = -2.08, P = .04). Conclusions: The DRS app has been shown to be a feasible and reliable tool to identify persons with UDPD and predict diabetes incidence in 2 years. The app can also encourage high-risk people to modify diet habits and reduce sedentary lifestyle.
Persistent Identifierhttp://hdl.handle.net/10722/253471
ISSN
2017 Impact Factor: 4.541
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLeung, YMA-
dc.contributor.authorXu, X-
dc.contributor.authorChau, PH-
dc.contributor.authorYu, YTE-
dc.contributor.authorCheung, MKT-
dc.contributor.authorWong, CKH-
dc.contributor.authorFong, DYT-
dc.contributor.authorWong, JYH-
dc.contributor.authorLam, CLK-
dc.date.accessioned2018-05-21T02:58:17Z-
dc.date.available2018-05-21T02:58:17Z-
dc.date.issued2018-
dc.identifier.citationJMIR mHealth and uHealth, 2018, v. 6 n. 5, p. e10662:1-e10662:10-
dc.identifier.issn2291-5222-
dc.identifier.urihttp://hdl.handle.net/10722/253471-
dc.description.abstractBackground: To decrease the burden of diabetes in society, early screening of undiagnosed diabetes and prediabetes (UDPD) is needed. Integrating a diabetes risk score into a mobile app provides a useful platform to enable people to self-assess the risk of diabetes with ease. Objective: The objectives of this study were to: (1) assess the profile of app users of the Diabetes Risk Score (DRS) mobile app, (2) determine the optimal cut-off value of the Finnish diabetes risk score (FINDRISC) to identify UDPD in the Chinese population, (3) estimate the chance of developing diabetes within 2 years of using the app, and (4) investigate high-risk app users’ lifestyle behavior change after ascertaining their risk level from the app. Methods: The study was divided into two phases. Phase 1 adopted a cross-sectional design. A descriptive analysis was performed on the app users’ profile. Cohen’s Kappa score was used to show the agreement between the risk level (as shown in the app) and HbA1c test results. Sensitivity, specificity, and area under curve were used to determine the optimal cut-off value of the DRS in this population. Phase 2 was a prospective cohort study, using an online survey to follow up the app users. Logistic regression model was used to estimate the chance of developing diabetes within 2 years after using the app. Paired t-tests were used to compare the high-risk app users’ lifestyle change. Results: A total of 13,289 people used the app from 28 August 2014 to 31 December 2016. After data cleaning, 4,549 of these were considered as valid data. The majority of users were males, and about 40% had tertiary education or above. The optimal value of the DRS for identifying persons with UDPD was recommended to be 9, with an area under the ROC curve of 0.67 (P < .01, 95% CI: 0.60-0.74), sensitivity of 0.70 (95% CI: 0.58-0.80), and specificity of 0.57 (95% CI: 0.47-0.66). At the 2-year follow up, people in the high-risk group had a higher chance of developing diabetes (OR 4.59, 95% CI: 1.01 to 20.81, P = .048) than the low-risk group. The high-risk app users improved their daily intake of vegetables (Baseline: M = 0.76, SD = 0.43; follow-up: M = 0.93, SD = 0.26; t (81) = -3.77, P < .001) and daily exercise (Baseline: M = 0.40, SD = 0.49; follow-up: M = 0.54, SD = 0.50; t(81) = -2.08, P = .04). Conclusions: The DRS app has been shown to be a feasible and reliable tool to identify persons with UDPD and predict diabetes incidence in 2 years. The app can also encourage high-risk people to modify diet habits and reduce sedentary lifestyle.-
dc.languageeng-
dc.publisherJMIR Publications, Inc. The Journal's web site is located at http://mhealth.jmir.org/-
dc.relation.ispartofJMIR mHealth and uHealth-
dc.rightsJMIR mHealth and uHealth. Copyright © JMIR Publications, Inc.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleA mobile app for identifying individuals with undiagnosed diabetes and prediabetes (UDPD) and changing behavior: A two-year prospective study-
dc.typeArticle-
dc.identifier.emailLeung, YMA: angleung@hku.hk-
dc.identifier.emailChau, PH: phpchau@hku.hk-
dc.identifier.emailYu, YTE: ytyu@hku.hk-
dc.identifier.emailWong, CKH: carlosho@hku.hk-
dc.identifier.emailFong, DYT: dytfong@hku.hk-
dc.identifier.emailWong, JYH: janetyh@hku.hk-
dc.identifier.emailLam, CLK: clklam@hku.hk-
dc.identifier.authorityLeung, YMA=rp00405-
dc.identifier.authorityChau, PH=rp00574-
dc.identifier.authorityYu, YTE=rp01693-
dc.identifier.authorityWong, CKH=rp01931-
dc.identifier.authorityFong, DYT=rp00253-
dc.identifier.authorityWong, JYH=rp01561-
dc.identifier.authorityLam, CLK=rp00350-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.2196/10662-
dc.identifier.hkuros285155-
dc.identifier.volume6-
dc.identifier.issue5-
dc.identifier.spagee10662:1-
dc.identifier.epagee10662:10-
dc.identifier.isiWOS:000433586200001-
dc.publisher.placeCanada-

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