Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality worldwide and in Australia. Although self-care is essential for CVD management, many individuals face barriers such as complex treatment regimens, limited health literacy, and lack of culturally appropriate support. Conversational agents (CAs) offer a promising solution by providing AI-driven, voice-based support that enables human-like interaction. However, most existing CAs primarily target mainstream populations, overlooking the needs of culturally and linguistically diverse communities. This study aimed to co-design and develop Diaspora Engaged Self-care Intervention (DESI-Heart), a culturally tailored CA to support self-care among individuals with CVDs from the Indian diaspora in Australia.
Methods
A participatory co-design study was conducted with members of the Indian diaspora in Australia living with Three online workshops were conducted using established design methods, including the ‘think-aloud’ approach, ‘bento-box reflections’, and the ‘MoSCoW’ method, to capture user preferences and inform culturally responsive intervention features. Workshop recordings were transcribed and thematically analysed using NVivo.
Results
Overall, the study included 15 individuals (aged ≥18 years) and identified six themes identified through the co-design process, which informed the design and key features of the DESI-Heart program. The themes included: (1) cultural context of self-care and unique ideas for the DESI-Heart program, (2) medication management features, (3) culturally appropriate dietary support, (4) physical activity support, (5) mental wellbeing support, and (6) user experience and implementation considerations.
Conclusions
This study provided insights into the needs of culturally and linguistically diverse populations, helping address cultural and linguistic barriers and inform the development and feasibility testing of the DESI-Heart program.
MensahGARothGAFusterV. The global burden of cardiovascular diseases and risk factors: 2020 and beyond. Washington, DC: American College of Cardiology Foundation, 2019, p. 2529–32.
2.
BoyleJPHoneycuttAANarayanKV, et al.Projection of diabetes burden through 2050: impact of changing demography and disease prevalence in the US. Diabetes Care2001; 24: 1936–1940.
WHO. Global action plan for the prevention and control of noncommunicable diseases 2013-2020. Geneva: World Health Organization, 2013.
8.
RiegelBMoserDKBuckHG, et al.Self-care for the prevention and management of cardiovascular disease and stroke: a scientific statement for healthcare professionals from the American Heart Association. J Am Heart Assoc2017; 6: e006997.
9.
WebberDGuoZMannS. Self-care in health: we can define it, but should we also measure it?Selfcare J2013; 4: 101–106.
10.
BaymotAGelaDBedadaT. Adherence to self-care recommendations and associated factors among adult heart failure patients in public hospitals, Addis Ababa, Ethiopia, 2021: cross-sectional study. BMC Cardiovasc Disord2022; 22: 75.
11.
KessingDDenolletJWiddershovenJ, et al.Psychological determinants of heart failure self-care: systematic review and meta-analysis. Psychosom Med2016; 78: 412–431.
12.
ZhangXMaHLamCY, et al.Effectiveness of acceptance and commitment therapy on self-care, psychological symptoms, and quality of life in patients with cardiovascular disease: a systematic review and meta-analysis. J Contextual Behav Sci2023; 29: 46–58.
13.
RiegelBCarlsonB. Facilitators and barriers to heart failure self-care. Patient Educ Couns2002; 46: 287–295.
14.
BartonHGrantMMitchamC, et al.Healthy urban planning in European cities. Health Promot Int2009; 24: i91–ii9.
15.
PagotoSBennettGG. How behavioral science can advance digital health. Transl Behav Med2013; 3: 271–276.
16.
Cruz-CoboCBernal-JiménezMÁVazquez-GarciaR, et al.Effectiveness of mHealth interventions in the control of lifestyle and cardiovascular risk factors in patients after a coronary event: systematic review and meta-analysis. JMIR Mhealth Uhealth2022; 10: e39593.
17.
WarePBartlettSJParéG, et al.Using eHealth technologies: interests, preferences, and concerns of older adults. Interact J Med Res2017; 6: e4447.
18.
XesfingiSVozikisA. eHealth literacy: in the quest of the contributing factors. Interact J Med Res2016; 5: e4749.
19.
Torrent-SellensJDíaz-ChaoÁSoler-RamosI, et al.Modelling and predicting eHealth usage in Europe: a multidimensional approach from an online survey of 13,000 European union internet users. J Med Internet Res2016; 18: e188.
20.
SirenAKnudsenSG. Older adults and emerging digital service delivery: a mixed methods study on information and communications technology use, skills, and attitudes. J Aging Soc Policy2017; 29: 35–50.
21.
KimBYLeeJ. Smart devices for older adults managing chronic disease: a scoping review. JMIR Mhealth Uhealth2017; 5: e7141.
22.
MoulikSChatterjeeS. DIL-A proof of concept study to show the efficacy of conversational agents for heart failure patients. J Strateg Innov Sustain2021; 16: 58–73.
23.
McTearMFCallejasZGriolD. The conversational interface. Switzerland: Springer, 2016.
24.
RadziwillNMBentonMC. Evaluating quality of chatbots and intelligent conversational agents. arXiv preprint arXiv:170404579. 2017.
25.
SinghBOldsTBrinsleyJ, et al.Systematic review and meta-analysis of the effectiveness of chatbots on lifestyle behaviours. NPJ Digit Med2023; 6: 18.
26.
WhiteheadLTalevskiJFatehiF, et al.Barriers to and facilitators of digital health among culturally and linguistically diverse populations. Qual Syst Rev J Med Internet Res2023; 25: e42719. PubMed PMID: 36853742.
27.
SinghBOldsTBrinsleyJ, et al.Systematic review and meta-analysis of the effectiveness of chatbots on lifestyle behaviours. NPJ Digit Med2023; 6: 18.
28.
Babington-AshayeAde MoerloosePDiopS, et al.Design, development and usability of an educational AI chatbot for people with haemophilia in Senegal. Haemophilia2023; 29: 1063–1073.
29.
GrossCSchachnerTHaslA, et al.Personalization of conversational agent-patient interaction styles for chronic disease management: two consecutive cross-sectional questionnaire studies. J Med Internet Res2021; 23: e26643.
30.
SchachnerTGrossCHaslA, et al.Deliberative and paternalistic interaction styles for conversational agents in digital health: procedure and validation through a web-based experiment. J Med Internet Res2021; 23: e22919.
31.
PokhrelPBennettBLRegmiS, et al.Individualism-collectivism, social self-control and adolescent substance use and risky sexual behavior. Subst Use Misuse2018; 53: 1057–1067.
32.
PhamTTLBerecki-GisolfJClappertonA, et al.Definitions of culturally and linguistically diverse (CALD): a literature review of epidemiological research in Australia. Int J Environ Res Public Health2021; 18: 37.
PayyappallimanaU. Health and well-being in Indian local health traditions. An integrated view of health and well-being: Bridging Indian and western knowledge. Springer, 2013, p. 99–112.
37.
KamathDYBhuvanaKSalazarLJ, et al.A qualitative, grounded theory exploration of the determinants of self-care behavior among Indian patients with a lived experience of chronic heart failure. PloS one2021; 16: e0245659.
38.
ConstantinNEdwardHNgH, et al.The use of co-design in developing physical activity interventions for older adults: a scoping review. BMC Geriatr2022; 22: 47.
39.
BernalGBonillaJBellidoC. Ecological validity and cultural sensitivity for outcome research: issues for the cultural adaptation and development of psychosocial treatments with Hispanics. J Abnorm Child Psychol1995; 23: 67–82.
40.
LiuDYHsuHF. An international comparison of empirical generalized double diamond model approaches to Taiwan and Korea. Competitiveness Review: An International Business Journal2009; 19: 160–174.
MagañaSLopezKAguinagaA, et al.Access to diagnosis and treatment services among Latino children with autism spectrum disorders. Intellect Dev Disabil2013; 51: 141–153.
43.
ValdezCRPadillaBMooreSM, et al.Feasibility, acceptability, and preliminary outcomes of the Fortalezas Familiares intervention for Latino families facing maternal depression. Fam Process2013; 52: 394–410.
44.
ColeACAdapaKKhasawnehA, et al.Codesign approaches involving older adults in the development of electronic healthcare tools: a systematic review. BMJ open2022; 12: e058390.
45.
KralikDPriceKTelfordK. The meaning of self-care for people with chronic illness. J Nurs Healthc Chronic Illn2010; 2: 197–204.
46.
HoneyMAspdenTBrackleyK, et al.Using a “think-aloud” process to aid co-design in development of a medicine information needs assessment tool. Online J Nurs Inform2022; 26: 7–1.
47.
SchnädelbachHCoughlanTKefalidouG, et al.Creativity Bento Box: a physical resource pack to support interaction in virtual space. Int J Hum Comput Interact2015; 31: 790–804.
48.
RaceyMAllistonPSherifaliD, et al.Co-designing a postpartum diabetes prevention program after gestational diabetes Mellitus: a MoSCoW prioritization workshop exercise. Diabetes Res Clin Pract2025; 225: 112269.
49.
DinglerTKwasnickaDWeiJ, et al.The use and promise of conversational agents in digital health. Yearb Med Inform2021; 30: 191–199.
50.
JansonsPRobinsLO'BrienL, et al.Gym-based exercise and home-based exercise with telephone support have similar outcomes when used as maintenance programs in adults with chronic health conditions: a randomised trial. J Physiother2017; 63: 154–160. PubMed PMID: 28655559.
51.
AhmadRSiemonDGnewuchU, et al.Designing personality-adaptive conversational agents for mental health care. Inf Syst Front2022; 24: 923–943.
52.
ClarkeVBraunVHayfieldN. Thematic analysis: a practical guide to research methods. Am Psychol Assoc 2015; 3: 222–248.
53.
ElliottV. Thinking about the coding process in qualitative data analysis. Qual Report2018; 23: 2850–2861.
54.
FlickU. The SAGE handbook of qualitative research design. 2022.
55.
FryerT. A critical realist approach to thematic analysis: producing causal explanations. J Crit Realism2022; 21: 365–384.
56.
YeomJ-WYeomI-SParkH-Y, et al.Cultural factors affecting the self-care of cancer survivors: an integrative review. Eur J Oncol Nurs2022; 59: 102165.
57.
RiegelBJaarsmaTLeeCS, et al.Integrating symptoms into the middle-range theory of self-care of chronic illness. Adv Nurs Sci2019; 42: 206–215.
58.
KellerCSCoeKMooreN. Addressing the demand for cultural relevance in intervention design. Health Promot Pract2014; 15: 654–663.
59.
Bin SawadANarayanBAlnefaieA, et al.A systematic review on healthcare artificial intelligent conversational agents for chronic conditions. Sensors2022; 22: 2625.
60.
StawarzKCoxALBlandfordA (eds.). Don't forget your pill! Designing effective medication reminder apps that support users’ daily routines. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 2014.
61.
KwanYHChengTYYoonS, et al.A systematic review of nudge theories and strategies used to influence adult health behaviour and outcome in diabetes management. Diabetes Metab2020; 46: 450–460.
62.
SantoKKirkendallSLabaT-L, et al.Interventions to improve medication adherence in coronary disease patients: a systematic review and meta-analysis of randomised controlled trials. Eur J Prev Cardiol2016; 23: 1065–1076.
63.
SohNLTouyzSWSurgenorLJ. Eating and body image disturbances across cultures: a review. Eur Eat Disord Rev: Prof J Eat Disord Assoc2006; 14: 54–65.
64.
Celis-MoralesCLivingstoneKMMarsauxCF, et al.Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Int J Epidemiol2017; 46: 578–588.
65.
LivingstoneKMLovePMathersJC, et al.Cultural adaptations and tailoring of public health nutrition interventions in indigenous peoples and ethnic minority groups: opportunities for personalised and precision nutrition. Proc Nutr Soc2023; 82: 478–486.
66.
KandulaNRDaveSSDe ChavezPJD, et al.An exercise intervention for South Asian mothers with risk factors for diabetes. Transl J Am Coll Sports Med2016; 1: 52–59.
67.
HorneMTierneySHendersonS, et al.A systematic review of interventions to increase physical activity among South Asian adults. Public Health2018; 162: 71–81.
68.
BhurjiNJaverJGasevicD, et al.Improving management of type 2 diabetes in South Asian patients: a systematic review of intervention studies. BMJ Open2016; 6: e008986.
69.
KhoslaPDoshiRHAminKS, et al.Faith-based anti-obesity program effects on BMI, diabetes, and hypertension in south Asian Americans: a prospective single-arm pre-post trial: Khosla. J Gen Intern Med2025; 41: 1–3.
70.
IslamNSZanowiakJMWyattLC, et al.Diabetes prevention in the New York city Sikh Asian Indian community: a pilot study. Int J Environ Res Public Health2014; 11: 5462–5486.
71.
NoordaliF. Mindfulness-based interventions for diabetes treatment and prevention in South Asian young adults. UK: University of Birmingham, 2018.
72.
King-ShierKMDhaliwalKKPuriR, et al.South Asians’ experience of managing hypertension: a grounded theory study. Patient Prefer Adherence2019; 13: 321–329.
73.
DuttAKNobleAG. The culture of India in spatial perspective: an introduction. India: cultural patterns and processe. Routledge, 2019, p. 1–28.
74.
OsokpoORiegelB. Cultural factors influencing self-care by persons with cardiovascular disease: an integrative review. Int J Nurs Stud2021; 116: 103383.
75.
SebastianATJansonsPNgEL, et al.Determining the feasibility and usability of a co-designed culturally appropriate conversational agent (DESI-Heart) to support self-care in people with cardiovascular diseases: protocol for a single-arm pilot trial. JMIR Res Protoc2025; 14: e78923.
