Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: protocol for a mixed-methods study

Yibo Wu, Hewei Min, Mingzi Li, Yuhui Shi, Aijuan Ma, Yumei Han, Yadi Gan, Xiaohui Guo, Xinying Sun, Yibo Wu, Hewei Min, Mingzi Li, Yuhui Shi, Aijuan Ma, Yumei Han, Yadi Gan, Xiaohui Guo, Xinying Sun

Abstract

Background: Patients with type 2 diabetes (T2DM) have an increasing need for personalized and Precise management as medical technology advances. Artificial intelligence (AI) technologies on mobile devices are being developed gradually in a variety of healthcare fields. As an AI field, knowledge graph (KG) is being developed to extract and store structured knowledge from massive data sets. It has great prospects for T2DM medical information retrieval, clinical decision-making, and individual intelligent question and answering (QA), but has yet to be thoroughly researched in T2DM intervention. Therefore, we designed an artificial intelligence-based health education accurately linking system (AI-HEALS) to evaluate if the AI-HEALS-based intervention could help patients with T2DM improve their self-management abilities and blood glucose control in primary healthcare.

Methods: This is a nested mixed-method study that includes a community-based cluster-randomized control trial and personal in-depth interviews. Individuals with T2DM between the ages of 18 and 75 will be recruited from 40-45 community health centers in Beijing, China. Participants will either receive standard diabetes primary care (SDPC) (control, 3 months) or SDPC plus AI-HEALS online health education program (intervention, 3 months). The AI-HEALS runs in the WeChat service platform, which includes a KBQA, a system of physiological indicators and lifestyle recording and monitoring, medication and blood glucose monitoring reminders, and automated, personalized message sending. Data on sociodemography, medical examination, blood glucose, and self-management behavior will be collected at baseline, as well as 1,3,6,12, and 18 months later. The primary outcome is to reduce HbA1c levels. Secondary outcomes include changes in self-management behavior, social cognition, psychology, T2DM skills, and health literacy. Furthermore, the cost-effectiveness of the AI-HEALS-based intervention will be evaluated.

Discussion: KBQA system is an innovative and cost-effective technology for health education and promotion for T2DM patients, but it is not yet widely used in the T2DM interventions. This trial will provide evidence on the efficacy of AI and mHealth-based personalized interventions in primary care for improving T2DM outcomes and self-management behaviors.

Trial registration: Biomedical Ethics Committee of Peking University: IRB00001052-22,058, 2022/06/06; Clinical Trials: ChiCTR2300068952, 02/03/2023.

Keywords: Artificial intelligence; Intelligent question and answering; Mixed-methods study; Mobile health; Type 2 diabetes.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2023. The Author(s).

Figures

Fig. 1
Fig. 1
Flow chart of patient recruitment and study implementation
Fig. 2
Fig. 2
The schedule of enrolment, interventions, and assessments

References

    1. International Diabetes Federation. IDF Diabetes Atlas teB, Belgium: International Diabetes Federation, 2021.
    1. Buse JB, Wexler DJ, Tsapas A, Rossing P, Mingrone G, Mathieu C, D'Alessio DA, Davies MJ. 2019 Update to: Management of Hyperglycemia in Type 2 Diabetes, 2018. a Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) Diabetes Care. 2020;43(2):487–493. doi: 10.2337/dci19-0066.
    1. Toi PL, Anothaisintawee T, Chaikledkaew U, Briones JR, Reutrakul S, Thakkinstian A. Preventive Role of Diet Interventions and Dietary Factors in Type 2 Diabetes Mellitus: An Umbrella Review. Nutrients. 2020;12(9):2722. doi: 10.3390/nu12092722.
    1. Papamichou D, Panagiotakos DB, Itsiopoulos C. Dietary patterns and management of type 2 diabetes: a systematic review of randomised clinical trials. Nutr Metab Cardiovasc Dis. 2019;29(6):531–543. doi: 10.1016/j.numecd.2019.02.004.
    1. Hemmingsen B, Gimenez-Perez G, Mauricio D, Roque IFM, Metzendorf MI, Richter B. Diet, physical activity or both for prevention or delay of type 2 diabetes mellitus and its associated complications in people at increased risk of developing type 2 diabetes mellitus. Cochrane Database Syst Rev. 2017;12:CD003054.
    1. Russell WR, Baka A, Bjorck I, Delzenne N, Gao D, Griffiths HR, Hadjilucas E, Juvonen K, Lahtinen S, Lansink M, et al. Impact of diet composition on blood glucose regulation. Crit Rev Food Sci Nutr. 2016;56(4):541–590. doi: 10.1080/10408398.2013.792772.
    1. Esposito K, Maiorino MI, Ciotola M, Di Palo C, Scognamiglio P, Gicchino M, Petrizzo M, Saccomanno F, Beneduce F, Ceriello A, et al. Effects of a Mediterranean-style diet on the need for antihyperglycemic drug therapy in patients with newly diagnosed type 2 diabetes: a randomized trial. Ann Intern Med. 2009;151(5):306–314. doi: 10.7326/0003-4819-151-5-200909010-00004.
    1. Esposito K, Maiorino MI, Petrizzo M, Bellastella G, Giugliano D. The effects of a Mediterranean diet on the need for diabetes drugs and remission of newly diagnosed type 2 diabetes: follow-up of a randomized trial. Diabetes Care. 2014;37(7):1824–1830. doi: 10.2337/dc13-2899.
    1. Jannasch F, Kroger J, Schulze MB. dietary patterns and Type 2 Diabetes: a systematic literature review and meta-analysis of prospective studies. J Nutr. 2017;147(6):1174–1182. doi: 10.3945/jn.116.242552.
    1. Schwingshackl L, Hoffmann G. Diet quality as assessed by the Healthy Eating Index, the Alternate Healthy Eating Index, the Dietary Approaches to Stop Hypertension score, and health outcomes: a systematic review and meta-analysis of cohort studies. J Acad Nutr Diet. 2015;115(5):780–800 e785. doi: 10.1016/j.jand.2014.12.009.
    1. Esposito K, Chiodini P, Maiorino MI, Bellastella G, Panagiotakos D, Giugliano D. Which diet for prevention of type 2 diabetes? a meta-analysis of prospective studies. Endocrine. 2014;47(1):107–116. doi: 10.1007/s12020-014-0264-4.
    1. Lean ME, Leslie WS, Barnes AC, Brosnahan N, Thom G, McCombie L, Peters C, Zhyzhneuskaya S, Al-Mrabeh A, Hollingsworth KG, et al. Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. Lancet. 2018;391(10120):541–551. doi: 10.1016/S0140-6736(17)33102-1.
    1. Lim EL, Hollingsworth KG, Aribisala BS, Chen MJ, Mathers JC, Taylor R. Reversal of type 2 diabetes: normalisation of beta cell function in association with decreased pancreas and liver triacylglycerol. Diabetologia. 2011;54(10):2506–2514. doi: 10.1007/s00125-011-2204-7.
    1. Gregg EW, Chen H, Wagenknecht LE, Clark JM, Delahanty LM, Bantle J, Pownall HJ, Johnson KC, Safford MM, Kitabchi AE, et al. Association of an intensive lifestyle intervention with remission of type 2 diabetes. JAMA. 2012;308(23):2489–2496. doi: 10.1001/jama.2012.67929.
    1. Elhayany A, Lustman A, Abel R, Attal-Singer J, Vinker S. A low carbohydrate Mediterranean diet improves cardiovascular risk factors and diabetes control among overweight patients with type 2 diabetes mellitus: a 1-year prospective randomized intervention study. Diabetes Obes Metab. 2010;12(3):204–209. doi: 10.1111/j.1463-1326.2009.01151.x.
    1. Rock CL, Flatt SW, Pakiz B, Taylor KS, Leone AF, Brelje K, Heath DD, Quintana EL, Sherwood NE. Weight loss, glycemic control, and cardiovascular disease risk factors in response to differential diet composition in a weight loss program in type 2 diabetes: a randomized controlled trial. Diabetes Care. 2014;37(6):1573–1580. doi: 10.2337/dc13-2900.
    1. Wheeler ML, Dunbar SA, Jaacks LM, Karmally W, Mayer-Davis EJ, Wylie-Rosett J, Yancy WS., Jr Macronutrients, food groups, and eating patterns in the management of diabetes: a systematic review of the literature, 2010. Diabetes Care. 2012;35(2):434–445. doi: 10.2337/dc11-2216.
    1. Barnard ND, Cohen J, Jenkins DJ, Turner-McGrievy G, Gloede L, Green A, Ferdowsian H. A low-fat vegan diet and a conventional diabetes diet in the treatment of type 2 diabetes: a randomized, controlled, 74-wk clinical trial. Am J Clin Nutr. 2009;89(5):1588S–1596S. doi: 10.3945/ajcn.2009.26736H.
    1. Viguiliouk E, Kendall CW, Kahleova H, Rahelic D, Salas-Salvado J, Choo VL, Mejia SB, Stewart SE, Leiter LA, Jenkins DJ, et al. Effect of vegetarian dietary patterns on cardiometabolic risk factors in diabetes: a systematic review and meta-analysis of randomized controlled trials. Clin Nutr. 2019;38(3):1133–1145. doi: 10.1016/j.clnu.2018.05.032.
    1. Soare A, Del Toro R, Khazrai YM, Di Mauro A, Fallucca S, Angeletti S, Skrami E, Gesuita R, Tuccinardi D, Manfrini S, et al. A 6-month follow-up study of the randomized controlled Ma-Pi macrobiotic dietary intervention (MADIAB trial) in type 2 diabetes. Nutr Diabetes. 2016;6(8):e222. doi: 10.1038/nutd.2016.29.
    1. Chiavaroli L, Lee D, Ahmed A, Cheung A, Khan TA, Blanco S, Mejia, Mirrahimi A, Jenkins DJA, Livesey G et al: Effect of low glycaemic index or load dietary patterns on glycaemic control and cardiometabolic risk factors in diabetes: systematic review and meta-analysis of randomised controlled trials. BMJ 2021, 374:n1651.
    1. Livesey G, Taylor R, Livesey HF, Buyken AE, Jenkins DJA, Augustin LSA, Sievenpiper JL, Barclay AW, Liu S, Wolever TMS, et al. Dietary Glycemic Index and Load and the Risk of Type 2 Diabetes: Assessment of Causal Relations. Nutrients. 2019;11(6):1436. doi: 10.3390/nu11061436.
    1. Livesey G, Taylor R, Livesey HF, Buyken AE, Jenkins DJA, Augustin LSA, Sievenpiper JL, Barclay AW, Liu S, Wolever TMS, et al. Dietary Glycemic Index and Load and the Risk of Type 2 Diabetes: A Systematic Review and Updated Meta-Analyses of Prospective Cohort Studies. Nutrients. 2019;11(6):1280. doi: 10.3390/nu11061280.
    1. Ojo O, Ojo OO, Adebowale F, Wang XH. The Effect of Dietary Glycaemic Index on Glycaemia in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Nutrients. 2018;10(3):373. doi: 10.3390/nu10030373.
    1. Boussageon R, Roustit M, Gueyffier F, Tudrej BV, Rehman MB. Type 2 diabetes. Lancet. 2018;391(10127):1261. doi: 10.1016/S0140-6736(18)30702-5.
    1. Collaboration NCDRF: Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet 2017, 390(10113):2627–2642.
    1. Chatterjee S, Khunti K, Davies MJ. Type 2 diabetes. Lancet. 2017;389(10085):2239–2251. doi: 10.1016/S0140-6736(17)30058-2.
    1. Smith AD, Crippa A, Woodcock J, Brage S. Physical activity and incident type 2 diabetes mellitus: a systematic review and dose-response meta-analysis of prospective cohort studies. Diabetologia. 2016;59(12):2527–2545. doi: 10.1007/s00125-016-4079-0.
    1. Umpierre D, Ribeiro PA, Kramer CK, Leitao CB, Zucatti AT, Azevedo MJ, Gross JL, Ribeiro JP, Schaan BD. Physical activity advice only or structured exercise training and association with HbA1c levels in type 2 diabetes: a systematic review and meta-analysis. JAMA. 2011;305(17):1790–1799. doi: 10.1001/jama.2011.576.
    1. Amanat S, Ghahri S, Dianatinasab A, Fararouei M, Dianatinasab M. Exercise and Type 2 Diabetes. Adv Exp Med Biol. 2020;1228:91–105. doi: 10.1007/978-981-15-1792-1_6.
    1. Balducci S, Sacchetti M, Haxhi J, Orlando G, D'Errico V, Fallucca S, Menini S, Pugliese G. Physical exercise as therapy for type 2 diabetes mellitus. Diabetes Metab Res Rev. 2014;30(Suppl 1):13–23. doi: 10.1002/dmrr.2514.
    1. Kanaley JA, Colberg SR, Corcoran MH, Malin SK, Rodriguez NR, Crespo CJ, Kirwan JP, Zierath JR. Exercise/physical activity in individuals with Type 2 Diabetes: a consensus statement from the American college of sports medicine. Med Sci Sports Exerc. 2022;54(2):353–368. doi: 10.1249/MSS.0000000000002800.
    1. Davies MJ, D'Alessio DA, Fradkin J, Kernan WN, Mathieu C, Mingrone G, Rossing P, Tsapas A, Wexler DJ, Buse JB. Management of Hyperglycemia in Type 2 Diabetes, 2018. a consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) Diabetes Care. 2018;41(12):2669–2701. doi: 10.2337/dci18-0033.
    1. Clark JE. Diet, exercise or diet with exercise: comparing the effectiveness of treatment options for weight-loss and changes in fitness for adults (18–65 years old) who are overfat, or obese; systematic review and meta-analysis. J Diabetes Metab Disord. 2015;14:31. doi: 10.1186/s40200-015-0154-1.
    1. Alonso-Dominguez R, Gomez-Marcos MA, Patino-Alonso MC, Sanchez-Aguadero N, Agudo-Conde C, Castano-Sanchez C, Garcia-Ortiz L, Recio-Rodriguez JI. Effectiveness of a multifactorial intervention based on an application for smartphones, heart-healthy walks and a nutritional workshop in patients with type 2 diabetes mellitus in primary care (EMID): study protocol for a randomised controlled trial. BMJ Open. 2017;7(9):e016191. doi: 10.1136/bmjopen-2017-016191.
    1. Siegel KR, Ali MK, Zhou X, Ng BP, Jawanda S, Proia K, Zhang X, Gregg EW, Albright AL, Zhang P. Cost-effectiveness of interventions to manage diabetes: has the evidence changed since 2008? Diabetes Care. 2020;43(7):1557–1592. doi: 10.2337/dci20-0017.
    1. Jiang X, Ming WK, You JH. The cost-effectiveness of digital health interventions on the management of cardiovascular diseases: systematic review. J Med Internet Res. 2019;21(6):e13166. doi: 10.2196/13166.
    1. de Batlle J, Massip M, Vargiu E, Nadal N, Fuentes A, Ortega Bravo M, Miralles F, Barbe F, Torres G. Group CO-L: implementing mobile health-enabled integrated care for complex chronic patients: intervention effectiveness and cost-effectiveness study. JMIR Mhealth Uhealth. 2021;9(1):e22135. doi: 10.2196/22135.
    1. Wu X, Guo X, Zhang Z. The efficacy of mobile phone apps for lifestyle modification in diabetes: systematic review and meta-analysis. JMIR Mhealth Uhealth. 2019;7(1):e12297. doi: 10.2196/12297.
    1. Lunde P, Nilsson BB, Bergland A, Kvaerner KJ, Bye A. The effectiveness of smartphone apps for lifestyle improvement in noncommunicable diseases: systematic review and meta-analyses. J Med Internet Res. 2018;20(5):e162. doi: 10.2196/jmir.9751.
    1. Holmen H, Torbjornsen A, Wahl AK, Jenum AK, Smastuen MC, Arsand E, Ribu L. A mobile health intervention for self-management and lifestyle change for persons with Type 2 Diabetes, part 2: one-year results from the norwegian randomized controlled trial renewing health. JMIR Mhealth Uhealth. 2014;2(4):e57. doi: 10.2196/mhealth.3882.
    1. Poppe L, De Bourdeaudhuij I, Verloigne M, Shadid S, Van Cauwenberg J, Compernolle S, Crombez G. Efficacy of a self-regulation-based electronic and mobile health intervention targeting an active lifestyle in adults having Type 2 Diabetes and in adults aged 50 years or older: two randomized controlled trials. J Med Internet Res. 2019;21(8):e13363. doi: 10.2196/13363.
    1. Frias J, Virdi N, Raja P, Kim Y, Savage G, Osterberg L. Effectiveness of digital medicines to improve clinical outcomes in patients with uncontrolled hypertension and Type 2 Diabetes: prospective, open-label, cluster-randomized pilot clinical trial. J Med Internet Res. 2017;19(7):e246. doi: 10.2196/jmir.7833.
    1. Hansel B, Giral P, Gambotti L, Lafourcade A, Peres G, Filipecki C, Kadouch D, Hartemann A, Oppert JM, Bruckert E, et al. A Fully automated web-based program improves lifestyle habits and HbA1c in patients with Type 2 Diabetes and abdominal obesity: randomized trial of patient e-coaching nutritional support (The ANODE Study) J Med Internet Res. 2017;19(11):e360. doi: 10.2196/jmir.7947.
    1. Eberle C, Lohnert M, Stichling S. Effectiveness of disease-specific mhealth apps in patients with diabetes mellitus: scoping review. JMIR Mhealth Uhealth. 2021;9(2):e23477. doi: 10.2196/23477.
    1. Abhari S, Niakan Kalhori SR, Ebrahimi M, Hasannejadasl H, Garavand A. Artificial Intelligence applications in Type 2 Diabetes mellitus care: focus on machine learning methods. Healthc Inform Res. 2019;25(4):248–261. doi: 10.4258/hir.2019.25.4.248.
    1. Kagawa R, Kawazoe Y, Ida Y, Shinohara E, Tanaka K, Imai T, Ohe K. Development of Type 2 Diabetes Mellitus phenotyping framework using expert knowledge and machine learning approach. J Diabetes Sci Technol. 2017;11(4):791–799. doi: 10.1177/1932296816681584.
    1. Zheng T, Xie W, Xu L, He X, Zhang Y, You M, Yang G, Chen Y. A machine learning-based framework to identify type 2 diabetes through electronic health records. Int J Med Inform. 2017;97:120–127. doi: 10.1016/j.ijmedinf.2016.09.014.
    1. Anderson AE, Kerr WT, Thames A, Li T, Xiao J, Cohen MS. Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: a cross-sectional, unselected, retrospective study. J Biomed Inform. 2016;60:162–168. doi: 10.1016/j.jbi.2015.12.006.
    1. Sudharsan B, Peeples M, Shomali M. Hypoglycemia prediction using machine learning models for patients with type 2 diabetes. J Diabetes Sci Technol. 2015;9(1):86–90. doi: 10.1177/1932296814554260.
    1. Cai J, Li C, Liu Z, Du J, Ye J, Gu Q, Xu J. Predicting DPP-IV inhibitors with machine learning approaches. J Comput Aided Mol Des. 2017;31(4):393–402. doi: 10.1007/s10822-017-0009-6.
    1. Zhang YF, Tian Y, Zhou TS, Araki K, Li JS. Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems. Comput Methods Programs Biomed. 2016;123:94–108. doi: 10.1016/j.cmpb.2015.09.020.
    1. Luo S, Chen S, Pan L, Zhang T, Han L, Wang Y, Safi QG. Exploring the effects of intervention for those at high risk of developing type 2 diabetes using a computer simulation. Comput Biol Med. 2014;53:105–114. doi: 10.1016/j.compbiomed.2014.05.015.
    1. Luo G. Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction. Health Inf Sci Syst. 2016;4:2. doi: 10.1186/s13755-016-0015-4.
    1. Lee BJ, Kim JY. Identification of Type 2 Diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on machine learning. IEEE J Biomed Health Inform. 2016;20(1):39–46. doi: 10.1109/JBHI.2015.2396520.
    1. Rau HH, Hsu CY, Lin YA, Atique S, Fuad A, Wei LM, Hsu MH. Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network. Comput Methods Programs Biomed. 2016;125:58–65. doi: 10.1016/j.cmpb.2015.11.009.
    1. Rigla M, Garcia-Saez G, Pons B, Hernando ME. Artificial intelligence methodologies and their application to diabetes. J Diabetes Sci Technol. 2018;12(2):303–310. doi: 10.1177/1932296817710475.
    1. Lan Y, He S, Liu K, Zeng X, Liu S, Zhao J. Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion. BMC Med Inform Decis Mak. 2021;21(Suppl 9):335. doi: 10.1186/s12911-021-01622-7.
    1. Chang D, Chen M, Liu C, Liu L: DiaKG: an Annotated Diabetes Dataset for Medical Knowledge Graph Construction. In China Conference on Knowledge Graph and Semantic Computing (pp 308–314) Springer, Singapore 2021.
    1. Huang X, Zhang J, Xu Z, Ou L, Tong J. A knowledge graph based question answering method for medical domain. PeerJ Comput Sci. 2021;7:e667. doi: 10.7717/peerj-cs.667.
    1. Yin Y, Zhang L, Wang Y, Wang M, Zhang Q, Li GZ. Question answering system based on knowledge graph in traditional chinese medicine diagnosis and treatment of viral Hepatitis B. Biomed Res Int. 2022;2022:7139904. doi: 10.1155/2022/7139904.
    1. Xiu X, Qian Q, Wu S. Construction of a digestive system tumor knowledge graph based on chinese electronic medical records: development and usability study. JMIR Med Inform. 2020;8(10):e18287. doi: 10.2196/18287.
    1. Singh Rawat BP, Li F, Yu H. Clinical judgement study using question answering from electronic health records. Proc Mach Learn Res. 2019;106:216–229.
    1. Chen Y SA, Chen C H, et al. : Personalized food recommendation as constrained question answering over a large-scale food knowledge graph. Proceedings of the 14th ACM International Conference on Web Search and Data Mining 2021: 544–552.
    1. Society CD: Guideline for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition). Chinese Journal of Diabetes Mellitus 2021, 13(4):315–409.
    1. Hayes RJ, Bennett S. Simple sample size calculation for cluster-randomized trials. Int J Epidemiol. 1999;28(2):319–326. doi: 10.1093/ije/28.2.319.
    1. Bot Factory22 Dialogue AI Platform Rapidly build your exclusive bot [] Accessed on 20 May 2023.
    1. Toobert DJ, Hampson SE, Glasgow RE. The summary of diabetes self-care activities measure: results from 7 studies and a revised scale. Diabetes Care. 2000;23(7):943–950. doi: 10.2337/diacare.23.7.943.
    1. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–1395. doi: 10.1249/01.MSS.0000078924.61453.FB.
    1. Kripalani S, Risser J, Gatti ME, Jacobson TA. Development and evaluation of the Adherence to Refills and Medications Scale (ARMS) among low-literacy patients with chronic disease. Value Health. 2009;12(1):118–123. doi: 10.1111/j.1524-4733.2008.00400.x.
    1. Spitzer RL, Kroenke K, Williams JB, Lowe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–1097. doi: 10.1001/archinte.166.10.1092.
    1. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613. doi: 10.1046/j.1525-1497.2001.016009606.x.
    1. Warttig SL, Forshaw MJ, South J, White AK. New, normative, English-sample data for the Short Form Perceived Stress Scale (PSS-4) J Health Psychol. 2013;18(12):1617–1628. doi: 10.1177/1359105313508346.
    1. Duong TV, Aringazina A, Kayupova G, Nurjanah, Pham TV, Pham KM, Truong TQ, Nguyen KT, Oo WM, Su TT et al: Development and validation of a new short-form health literacy instrument (HLS-SF12) for the general public in six asian countries. Health Lit Res Pract 2019, 3(2):e91-e102.
    1. Herdman M, Gudex C, Lloyd A, Janssen M, Kind P, Parkin D, Bonsel G, Badia X. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L) Qual Life Res. 2011;20(10):1727–1736. doi: 10.1007/s11136-011-9903-x.

Source: PubMed

Szponzorok és közreműködők

Egészségi állapot

Kábítószer-beavatkozások

3
Iratkozz fel