Abstract
Objectives
To assess the knowledge of community dwelling adults on stroke risk and their willingness to use mobile health (mHealth) technology in assessing their stroke risk.
Materials and Methods
A cross-sectional study was conducted among adults (≥18 years old) using survey questionnaires designed by neurologists and health promotion experts and administered by trained study staff. Logistic regression models were used to assess factors associated with receptivity toward knowing individual stroke risk score and willingness to use a mobile application (App) to assess stroke risk.
Results
The survey was administered to 486 participants in Nigeria, with a mean age of 47.4 ± 15.5 years, comprising 53.5% females. Up to 84% of participants wanted to know their risk for developing stroke but only 29.6% of respondents had ever previously had their stroke risk assessed. Factors associated with willingness to know stroke risk were increasing age [aOR (95% CI): 0.97 (0.95 – 0.99)], and Hausa tribe [16.68 (2.16 – 128.92)]. Up to 66% of participants wanted to know their immediate risk of stroke, compared with 6.6% and 2.1% who wanted to know their 5-year or 10-year future stroke risks respectively. Regarding locations, participants preferred stroke risk assessment to be performed at a health facility, at home by health professional, on their own using mHealth (stroke risk calculator application), or at communal gatherings (decreasing order). About 70% specifically wished to learn about their stroke risk via an mHealth application.
Conclusions
Community dwelling Nigerians wanted to know their immediate risk of stroke using digital platforms, such as a mobile phone stroke risk calculator application. Clinical trials are needed to assess the effectiveness of such a strategy for primary prevention of stroke in sub-Saharan African communities.
Keywords: Perspectives, Preferences, Risk Assessment, Stroke Prevention, Sub-Saharan African
Introduction
Stroke is the second leading cause death and the third leading cause of combined deaths and disability globally, with a disproportionately high burden in low-and-middle income countries (LMICs).1 In 2019, the age-standardized stroke-related mortality rate and disability-adjusted life-years (DALY) were 3.6 times and 3.7 times higher in low-income countries than high-income regions.1 Although 90% of strokes can be averted through control of modifiable risk factors2, LMICs in particular are saddled with abysmally low rates of awareness, detection, treatment and control of stroke risk factors.3–5 With paltry national health budgets and modest health workforce for management of incident and recurrent strokes, most LMICs should commit greater investments towards primordial and primary prevention of stroke.6
Community health education about stroke and its risk factors is a strategy for cultivating the attitudes and skills needed by the population and health systems.7 A particularly appealing approach for mass dissemination of stroke risk health information is through mobile applications (Apps). The unprecedented expansion of telecommunication and the widespread penetration of mobile phones, including most remote regions of the globe, have provided promising avenues for rapid diffusion and accessibility of health information to community members. Furthermore, coupling health information with behavioral interventions, such as ability to assess one’s personal health risk profile for stroke, is an added advantage of digital health.8,9
Sub-Saharan Africa (SSA) typifies a global regional block with an astronomical rise in stroke burden, afflicting a significantly younger demographic profile for which solutions with respect to primary stroke prevention are emergent.10–15 A stroke occurs every 6 minutes in SSA largely driven by a lack of awareness, detection and control of stroke risk factors.13 To stem the rising tide of stroke in SSA, population education to promote awareness and control key vascular risk factors would be paramount. Indeed, some of the key systems for mass stroke risk education via mobile health approaches are in place with an estimated 80–90% mobile phone uptake and active engagement with social media platforms in Africa.16 Furthermore, the dominant vascular risk factors for incident and recurrent stroke by its primary types are now well characterized among indigenous Africans.17–21 In addition, empirical data on the effect of mHealth interventions for blood pressure control from clinical trials for prevention of stroke are now available from SSA.22–24 However, the perspectives of community dwelling members25 from SSA on the potential role of digital modalities for delivering stroke educational material conjointly with personalized and individualized risk factor screening has hitherto not been explored for primary stroke risk reduction. Our objective for the present study was therefore to assess the knowledge and perspectives of community dwelling adults on stroke risk and its assessment via mobile health technology. Receptivity especially toward an mHealth stroke-risk-o-meter App was assessed using surveys administered among stroke free adults in Nigeria.
Methods
Study design and participants:
This study was a cross-sectional study conducted as part of our ongoing community outreach activities with the Stroke Investigative Research and Educational Network (SIREN) and Systematic Investigation of Blacks with Stroke (SIBS) Genomics 26,27 studies in Nigeria. Stroke-free controls were randomly selected from the SIREN and SIBS Genomics cohorts and invited by our Community Engagement Core members to complete study surveys at local community centers within Ibadan, Zaria and Abeokuta in Nigeria between May and June 2022. All stroke-free controls were previously screened for absence of symptoms for stroke using a locally validated version of the questionnaire for verifying stroke free status.28
Design and administration of questionnaires:
A structured interviewer-administered survey questionnaire was designed and used to collect vital information on the sociodemographic characteristics of the study participants, their knowledge, and perspectives on using a mobile phone technology to assess stroke risk factors. The content of the questionnaire was first developed by MN, with inputs by the study team comprising of neurologists, qualitative methodologists, health promotion experts, biostatisticians, and media strategists with practical experience in social media marketing and impact metrics. These experts assessed face validity of the survey questions by evaluating whether questions effectively captured the topics under investigation. Up to five iterative virtual meetings led by MO were held by the study team to review and refine study questions using a Delphi methodology until convergence was achieved. CJ checked the questionnaire construction for common errors, such as leading and double-barreled questions. Piloting of the questionnaire was done with one male and one female stroke-free control at two different time points to assess potential difficulties with working and comprehension. The final questionnaire was translated from English to Yoruba and Hausa languages using standard procedure including back-translation and administered by trained interviewers. Paper-based survey data were entered into REDCap, a secure database, and transcription accuracy was confirmed prior to data analysis.
Sample size determination: The survey aimed to assess the perspectives of community stroke-free persons on stroke risk assessment via mobile applications. Being a cross-sectional survey, a sample size of 385 participants was deemed sufficient to determine the required proportion with a precision of 5% and a 95% confidence level. Adjusting for a non-response rate of 15%, the final sample calculation was 454.
Minimizing bias: Bias was prevented by selecting a random sample of stroke-free participants from our existing database of SIREN controls.
Statistical methods
Socio-demographic characteristics were summarized using frequency and percentages while mean and standard deviation (sd) were computed for age. Perceptions on stroke, risk assessment, and use of mobile health application were presented using frequency and percentages. Chi-square test and independent t-test were used to investigate factors associated with the wish for participants to know about stroke risk and use of mobile app. Subsequently, binary logit model was fitted to identify independent factors associated with willingness to know about stroke risk as well as use of mobile app. Adjusted Odds Ratio (AOR) was estimated with 95% confidence interval. Residual analysis and Hosmer-Lemeshow test were used for assessment of the logit models.
Results:
Demographic characteristics:
The survey was administered to 486 participants in Nigeria, with a mean age of 47.7 ± 15.5 years, comprising of 53.5% females. There were 200 participants from Ibadan, 36 from Abeokuta and 250 from Zaria. Over 44% of participants had attained tertiary or higher level of education, and most were employed (Table 1).
Table 1: Sociodemographic characteristics of respondents.
| Socio-demographic characteristics (n=486) | Frequency (%) |
|---|---|
| Age (mean ± SD) years | 47.7 ± 15.5 |
| Sex | |
| Male | 221 (46.5) |
| Female | 254 (53.5) |
| Educational status | 54 (11.1) |
| None | |
| Primary | 69 (14.2) |
| Secondary | 142 (30.0) |
| Tertiary or higher | 210 (44.7) |
| Occupation | |
| Skilled Professional | 72 (14.8) |
| Skilled | 106 (22.0) |
| Semi-skilled | 95 (19.6) |
| Manual worker | 89 (18.9) |
| Non-paid worker | 0 (0.0) |
| Retired | 31 (7.8) |
| Unemployed | 82 (16.9) |
| Religion | |
| Christian | 230 (48.4) |
| Islam | 245 (51.6) |
| Traditional | 0 (0.0) |
| Ethnicity | |
| Yoruba | 265 (56.6) |
| Igbo | 24 (4.9) |
| Hausa | 111 (22.8) |
| Others | 75 (15.6) |
Perceptions on stroke and its risk assessment:
Over 87% of survey participants had heard about stroke as a disease before, and 81.3% had heard about factors which increased an individual’s chances of developing a stroke. Up to 84% of participants wanted to know their risk for developing stroke, with an overwhelming majority (96.1%) indicating that such information would help them decide what steps to take to mitigate their stroke risk. However, only 30.4% of respondents had ever had their stroke risk assessed. Reasons for those who had checked their stroke risk are shown in Table 2. Up to 66% of participants wanted to know their immediate risk of stroke, compared with 6.4% and 2.1% who wanted to know their future stroke risk in the next 5 or 10 years respectively. Participants preferred stroke risk assessment to be performed at a hospital or clinic, at home by health professional, on their own using mHealth, or at communal gatherings (decreasing order of preference).
Table 2: Knowledge of stroke and its risk assessment.
| Knowledge of stroke risk | Frequency (%) |
|---|---|
| Have you heard about stroke as a disease? | |
| Yes | 424 (87.2) |
| No | 62 (12.8) |
| Has anyone in your family had a stroke? | |
| Yes | 74 (17.5) |
| No | 349 (82.5) |
| Have you heard about things that increases or decreases your chance for developing a stroke? | |
| Yes | 344 (81.3) |
| No | 56 (13.2) |
| Not sure | 24 (5.7) |
| Would you want to know if you have a risk or a chance for developing a stroke? | |
| Yes | 355 (83.7) |
| No | 56 (13.2) |
| Maybe | 13 (3.1) |
| Reasons for wanting to know | |
| It will help me know what to do | 365 (96.1) |
| Others | 15 (3.9) |
| Have you ever had your risk for stroke checked before? | |
| Yes | 129 (30.4) |
| No | 244 (57.6) |
| Not sure | 35 (8.3) |
| I don’t know | 16 (3.8) |
| If yes, why did you check? | |
| I noticed some strange signs in my body | 15 (11.6) |
| Advised by my Doctor | 22 (17.1) |
| Advised by friend, relation or colleague | 6 (4.7) |
| Heard about the need to check on the radio or TV | 18 (14.0) |
| Heard about the need to check on the social media, etc. | 4 (3.1) |
| Other reason | 64 (49.6) |
| If no, why? | |
| I don’t think it is important | 44 (18.1) |
| I have never heard about it | 31 (12.7) |
| I want to but I don’t know where to do the check | 78 (32.0) 91 (37.3) |
| If we were able to tell you your chances of developing a stroke, would you prefer to know your risk | |
| Now? | 280 (66.0) |
| In the next 5 years? | 27 (6.4) |
| In the next 10 years? | 9 (2.1) |
| In the next 20 years? | 1 (0.2) |
| Both now and future risk? | 37 (8.7) |
| I don’t know | 70 (16.5) |
| If you want to know your chances of developing a stroke in the future (5, 10 or 20 years), what are your reasons for this choice? | |
| Where would you be willing to have your stroke risk checked? Rank: Median (IQR) | |
| Hospital/clinic | 1 (1,2) |
| Communal gathering such as church, or mosque or others | 3 (3,4) |
| My home by a healthcare professional | 2 (2,3) |
| On my own, using my knowledge of my medical history on my mobile phone | 3 (2, 4) |
Factors associated with participants wish to know about stroke risk:
Table 3 compares key demographic characteristics of participants who wanted to know their stroke versus those who did not want to know. Unadjusted analysis identified potential associations between participants’ wish to know their individual risk of stroke and age, sex, education, employment status, and ethnicity. Manual work was associated with a lower adjusted odds of wish to know stroke risk with aOR (95% CI) of 0.22 (0.05 – 0.97) while Hausa tribe was associated with a higher odd of wish to know stroke risk 16.25 (2.10 – 125.23) (Table 4).
Table 3: Demographic characteristics associated with participants wish to know about the chance of developing a stroke.
| Socio-demographic characteristics | Wish to know one’s stroke risk | X2 / t | P-value | |
|---|---|---|---|---|
| Yes | No | |||
| Age (mean ± SD) years | 45.6±15.1 | 56.4±13.7 | 6.25 | <0.01 |
| Sex | ||||
| Male | 202 (89.4) | 24 (10.6) | ||
| Female | 191 (73.8) | 68 (26.3) | 19.19 | <0.01 |
| Educational status | ||||
| None | 31 (58.5) | 22 (41.5) | 54.77 | <0.01 |
| Primary | 42 (60.9) | 27 (39.1) | ||
| Secondary | 119 (81.5) | 27 (18.5 | ||
| Tertiary or higher | 201(92.6) | 16 (7.4) | ||
| Occupation | ||||
| Skilled professional | 68 (94.4) | 4 (5.6) | 89.77 | <0.01 |
| Skilled | 97 (90.7) | 10 (9.4) | ||
| Semi-skilled | 80 (84.2) | 15 (15.8) | ||
| Manual worker | 44 (47.8) | 48 (52.2) | ||
| Retired | 32 (84.2) | 6 (15.8) | ||
| Unemployed | 72 (88.9) | 9 (11.1) | ||
| Religion | ||||
| Christian | 188 (80.0) | 47 (20.0) | 0.32 | 0.57 |
| Islam | 205 (82.0) | 45 (18.0) | ||
| Ethnicity | ||||
| Yoruba | 192 (70.7) | 82 (29.3) | 52.66 | <0.01 |
| Igbo | 23 (95.8) | 1 (4.2) | ||
| Hausa | 110 (99.1) | 1 (0.9) | ||
| Others | 68 (89.5) | 8 (10.5) | ||
| Family history of stroke; Yes | 62 (87.3) | 9 (12.2) | 3.98 | 0.137 |
Table 4: Factors associated with participants wish to know about the chance of developing a stroke.
| Socio-demographic characteristics | Crude OR (95% CI) | P-value | Adjusted OR (95% CI) | P-value |
|---|---|---|---|---|
| Age (mean ± SD) years | 0.96 (0.94, 0.98) | <0.01 | 0.97 (0.95, 0.98) | 0.057 |
| Sex | ||||
| Male (ref) | ||||
| Female | 0.42 (0.23, 0.77) | 0.005 | 0.65 (0.33, 1.28) | 0.211 |
| Educational status | ||||
| None (ref) | ||||
| Primary | 1.15 (0.42, 3.23) | 0.778 | 0.93 (0.28, 2.95) | 0.881 |
| Secondary | 1.84 (0.76, 4.48) | 0.205 | 0.97 (0.33, 2.80) | 0.728 |
| Tertiary or higher | 5.68 (2.19, 14.69) | <0.01 | 1.35 (0.34, 5.37) | 0.672 |
| Occupation | ||||
| Skilled professional (ref) | ||||
| Skilled | 0.72 (0.21, 2.50) | 0.620 | 0.81 (0.22, 2.95) | 0.805 |
| Semi-skilled | 0.53 (0.15, 1.85) | 0.319 | 1.27 (0.27, 5.89) | 0.634 |
| Manual worker | 0.08 (0.03, 0.25) | <0.01 | 0.22 (0.05, 0.97) | 0.046 |
| Retired | 0.39 (0.09, 1.57) | 0.186 | 1.49 (0.30, 7.53) | 0.312 |
| Unemployed | 1.06 (0.25, 4.42) | 0.935 | 1.76 (0.35, 8.74) | 0.490 |
| Religion | ||||
| Christian (ref) | ||||
| Islam | 0.98 (0.55, 1.72) | 0.953 | ||
| Ethnicity | ||||
| Yoruba (ref) | ||||
| Igbo | 5.61 (0.74, 43.86) | 0.096 | 3.26 (0.36, 29.21) | 0.290 |
| Hausa | 28.88 (3.93, 212.51) | 0.001 | 16.25 (2.10, 125.23) | 0.007 |
| Others | 2.24 (1.00, 4.98) | 0.049 | 0.85 (0.33, 2.21) | 0.738 |
| Family history of stroke; Yes | 1.21 (0.57, 2.61) | 0.614 |
Perspectives on use of mobile health applications and cost implications for stroke risk screening (Table 5):
Table 5: Perspectives on Use of Mobile health applications and cost considerations for Stroke risk screening.
| Stroke screening | Frequency (%) |
|---|---|
| Willingness to use a mobile health application to learn about stroke risk | |
| Yes | 292 (68.9) |
| Maybe | 36 (8.5) |
| Not sure | 30 (7.1) |
| No | 66 (15.5) |
| How would you prefer for the cost of stroke risk screening to be covered? | |
| Out-of-pocket | 43 (14.7) |
| By private insurance | 40 (13.7) |
| By Government | 192 (65.8) |
| I don’t know | 17 (5.8) |
| Do you find this acceptable to be bled for the purpose of stroke risk factor screening? | |
| Very acceptable | 214 (50.5) |
| Acceptable | 171 (40.3) |
| Neutral | 25 (5.9) |
| Unacceptable | 14 (3.3) |
| If able to learn of your stroke risk factors and what to do to lower your risk of stroke, how likely would you be willing to make changes in your health? | |
| Very likely | 282 (66.5) |
| Likely | 82 (19.3) |
| May be | 38 (9.0) |
| Not likely | 18 (4.3) |
| Very unlikely | 4 (0.9) |
| What means are available to you to cover the cost of stroke risk control? | |
| Out-of-pocket | 155 (36.6) |
| By private insurance | 43 (10.1) |
| By Government | 190 (44.8) |
| I don’t know | 36 (8.5) |
| How would you prefer for the cost of stroke risk control to be covered? | |
| Out-of-pocket | 51 (12.0) |
| By private insurance | 35 (8.3) |
| By Government | 304 (71.7) |
| I don’t know | 28 (6.6) |
| Where do you currently receive or learn about health-related information? (Multiple responses) | |
| Doctor/nurse | 399 (82.1) |
| Community health worker | 118 (24.3) |
| Community leader (religious leader, faith healer, or other) | 48 (9.9) |
| Radio | 125 (25.7) |
| Television | 157 (32.3) |
| YouTube | 85 (17.5) |
| What social media platforms do you currently use? (Multiple responses) | |
| 315 (64.8) | |
| 244 (50.2) | |
| 98 (20.2) | |
| YouTube | 125 (25.7) |
| 38 (7.8) | |
| TikTok | 55 (11.3) |
| 93 (19.1) | |
| Telegram | 97 (20.0 |
| How much time do you spend on the social media? | |
| 1 minute | 13 (3.8) |
| 30 minutes | 115 (33.4) |
| An hour | 92 (26.7) |
| 2 hours | 55 (16.0) |
| 3 hours | 31 (9.0) |
| 5 hours and above | 38 (11.1) |
| How useful is the social media for learning health information? | |
| Very useful | 278 (79.7) |
| Not useful | 21 (6.0) |
| Not sure | 50 (14.3) |
| To what extent can the social media influence your health habits? | |
| To a large extent | 246 (52.8) |
| Seldom | 77 (16.5) |
| Never | 143 (30.7) |
Among respondents, 68.9% indicated a wish to learn about their stroke risk via a mobile health application. Participants opined that the cost of stroke risk screening should be borne by the government (65.8%). Concerning the responsible agencies or parties to cover the cost of stroke risk factor control, respondents gave varied responses including government (44.8%), out-of-pocket payments (36.6%), insurance (10.1%), and not sure (8.5%). A significant majority of respondents (82.1%) received or learned about health-related information from their doctor or nurse, followed by television (32.3%), and radio (25.7%) (Table 5). Respondents reported using various social media platforms, the top three being WhatsApp (64.8%), Facebook (50.2%), and YouTube (25.7%).
Demographic characteristics associated with a wish to use a mobile health App to learn about stroke risk:
A comparison of socio-demographic features according to wish to use a mHealth App to learn about stroke risk is presented in Table 6. Younger age, tertiary education, manual workers and retired sub-categories under occupational status were associated in unadjusted analysis (Table 7). These were mostly lost upon adjustment for confounders with only manual workers [adjusted odds ratio of 0.27 (95%CI: 0.10 – 0.73)] and those unsure of family history of stroke [adjusted OR 0.19 (0.07–0.53)] independently maintaining association with a lower wish to use an App to learn about stroke risk.
Table 6: Demographic characteristics associated with wish to use a mobile health application to learn about stroke risk.
| Socio-demographic characteristics | Mobile health App use | X2 / t | P-value | |
|---|---|---|---|---|
| Yes | No | |||
| Age (mean ± SD) years | 45.9±14.6 | 51.0±16.6 | 3.43 | <0.01 |
| Sex | ||||
| Male | 155 (68.6) | 70 (31.4) | ||
| Female | 169 (65.0) | 89 (35.0) | 0.698 | 0.403 |
| Educational status | ||||
| None | 29 (53.7) | 25 (46.3) | 35.07 | <0.01 |
| Primary | 28 (40.6) | 41 (59.4) | ||
| Secondary | 101 (69.2) | 45 (30.8) | ||
| Tertiary or higher | 166 (76.5) | 51 (23.5) | ||
| Occupation | ||||
| Skilled professional | 56 (77.8) | 16 (22.2) | 81.51 | <0.01 |
| Skilled | 79 (73.8) | 28 (26.1) | ||
| Semi-skilled | 82 (86.3) | 13 (13.7) | ||
| Manual worker | 28 (30.4) | 64 (69.6) | ||
| Retired | 23 (60.3) | 15 (39.5) | ||
| Unemployed | 56 (68.3) | 26 (31.7) | ||
| Religion | ||||
| Christian | 166 (70.6) | 69 (29.4) | 3.23 | 0.07 |
| Islam | 158 (63.0) | 93 (37.1) | ||
| Ethnicity | ||||
| Yoruba | 183 (66.6) | 92 (33.4) | 1.51 | 0.679 |
| Igbo | 15 (62.5) | 9 (37.5) | ||
| Hausa | 71 (64.0) | 40 (36.0) | ||
| Others | 55 (72.4) | 21 (27.6) | ||
| Family history of stroke; Yes No Not sure |
55 (74.3) 230 (70.6) 7 (30.4) |
19 (25.7) 96 (29.4) 16 (69.6 |
17.35 | P<0.01 |
Table 7: Factors associated with wish of participants to use a mobile health application to learn about stroke risk.
| Socio-demographic characteristics | Crude OR (95% CI) | P-value | Adjusted OR (95% CI) | P-value |
|---|---|---|---|---|
| Age (mean ± SD) years | 0.98 (0.96, 0.99) | 0.005 | .00 (0.98, 1.02) | 0.833 |
| Sex | ||||
| Male (ref) | ||||
| Female | 1.00 (0.66, 1.51) | 0.883 | ||
| Educational status | ||||
| None (ref) | ||||
| Primary | 0.63 (0.25, 1.55) | 0.314 | 0.70 (0.24, 1.99) | 0.500 |
| Secondary | 2.18 (1.00, 4.71) | 0.049 | 2.16 (0.85, 5.45) | 0.105 |
| Tertiary or higher | 3.18 (1.49, 6.76) | 0.003 | 2.52 (0.88, 7.21) | 0.083 |
| Occupation | ||||
| Skilled professional (ref) | ||||
| Skilled | 0.79 (0.38, 1.66) | 0.617 | 0.89 (0.41, 1.92) | 0.695 |
| Semi-skilled | 1.25 (0.54, 2.89) | 0.597 | 2.68 (0.95, 7.55) | 0.062 |
| Manual worker | 0.13 (0.06, 0.29) | <0.01 | 0.27 (0.10, 0.73) | 0.010 |
| Retired | 0.37 (0.15, 0.90) | 0.028 | 0.49 (0.17, 1.49) | 0.139 |
| Unemployed | 0.55 (0.26, 1.19) | 0.130 | 0.74 (0.32, 1.71) | 0.479 |
| Religion | ||||
| Christian (ref) | ||||
| Islam | 0.64 (0.42, 0.97) | 0.034 | 0.73 (0.46, 1.18) | 0.200 |
| Ethnicity | ||||
| Yoruba (ref) | ||||
| Igbo | 0.73 (0.29, 1.82) | 0.496 | ||
| Hausa | 0.75 (0.46, 1.22) | 0.239 | ||
| Others | 1.07 (0.59, 1.91) | 0.823 | ||
| Family history of stroke. | ||||
| Yes | 1.21 (0.68, 2.14) | 0.518 | 1.44 (0.77, 2.72) | 0.256 |
| Not sure | 0.18 (0.08, 0.46) | <0.01 | 0.19 (0.07, 0.53) | 0.001 |
Discussion
In this cross-sectional, multicenter study among Nigerians, we found that approximately 84% of study participants wished to know their risk of developing a stroke. Approximately two-thirds of survey respondents indicated a desire to know their immediate rather than long-term future stroke risk, with a similar proportion (2/3) willing to use mobile health approaches to assess their stroke risk. In decreasing order, the preferred location for stroke risk assessment was at a health facility firstly, or at home by a health professional, followed by self-assessment by participants using a mobile phone and finally at communal gatherings such as church or mosques. Furthermore, two-thirds indicated that the cost of stroke risk factor screening should be supported by the government. On a salutary note, approximately 9 in 10 survey participants had previously heard about stroke and 8 in 10 had heard of a stroke risk factor before. These rates are higher than stroke awareness data from Malawi29, Uganda30, Ghana31 and Benin.32 A potential explanation for this rather high level of stroke risk awareness is that participants in our survey were recruited from a pool of stroke-free survivors who had served as controls for our previous SIREN case-control study.27
Our observation that approximately 84% of participants wanted to know about their risk for stroke suggests that the risk perception of stroke in our sample population is quite high. Of note, up to 17% of respondents had a family member who had suffered a stroke, reflecting the high burden of stroke in the region. There was an inverse association between age and a wish to know individual stroke risk with older participants, indicating a lower desire to know their stroke risk. Each 10-year rise in age was associated with a 3% (95%CI: 0–5%) lower adjusted odds of willingness to know stroke risk. This is a perplexing observation given the potent, dose association between increasing age and stroke occurrence. Perhaps, younger individuals are more frightened about the prospects of the devastating consequences of stroke and wish to identify ways to avert it. Further qualitative studies are warranted to better understand these associations between age and wish to know one’s risk for stroke in this population. How an individual perceives their risk of stroke may influence their decision to engage in risk reduction behaviors to avert a stroke or inform decisions to seek medical care promptly upon developing stroke symptoms. Despite the high level of willingness to know their stroke risk (84%), only about 30% of study participants reported prior stroke risk screening activity. This highlights the inconsistent connections between a wish to know one’s stroke risk and the availability of opportunities and concordant behavioral changes to assess and reduce the perceived risk.
A rather intriguing finding from the survey was a preference of participants to have knowledge of their immediate as opposed to a future risk of stroke based on their risk factor profile. Traditionally, the concept of cardiovascular (CVD) risk prognostication is premised on the presence of atherosclerotic diseases (stroke or myocardial infarction), atherosclerotic cardiovascular disease (ASCVD) equivalents, such as diabetes mellitus, peripheral arterial disease or presence of 2 or more vascular risk factors.33,34 ASCVD risk is often expressed as 5- or 10-year risk or lifetime risk, which may not convey a sense of urgency needed for behavioral changes needed to address CVD risk. Our experience in Ghana and Nigeria is that stroke risk prediction models developed using Western cohorts do not predict stroke risk in our population where incident stroke occurs at a much younger age. There is therefore the need to create stroke prediction models using datasets from indigenous Africans such as those available from the SIREN study. A percentage ranking based on cardiovascular risk profile from the dominant stroke risk factors in the SIREN study has been used to develop an Afrocentric stroke risk prediction score.35
Nearly 69% of study participants endorsed mobile or digital modalities for promotion of health education on stroke and risk factor screening. This indicates that social media platforms (especially WhatsApp) could be utilized for disseminating information about stroke risk estimation and reduction in our settings. None of the major demographic or socioeconomic indicators were independently associated with a wish to use mobile health applications to learn about stroke risk, except manual workers who indicated a lower willingness. The favorable response rate for mHealth approaches for stroke risk assessment should be carefully balanced with the wish of participants to have their stroke risk assessed either at a health facility or at home by a health professional. Indeed, the use of mobile phone Apps for self-assessment of stroke risk ranked third among 4 options in terms of location, but first among mobile health options. While the first two options are desirable, they may not be feasible for the huge population of people at risk of stroke (with high prevalence of stroke risk factors) across Africa given the profound shortage of personnel in the African health care systems.36 Therefore, digital tools could be deployed as a self-efficacy tool leveraging the high availability and access to mobile phones in most of the population.6,8 Using digital tools, lifestyle risk factors can be self-assessed while other factors can be scored based on personal medical history. This will promote health-seeking behaviour in individuals motivating them to assess other risk factors (such as blood pressure, blood glucose and lipid levels) if not already known, in relevant facilities or with available reliable point-of-care devices.6,8 Digital tools could serve as a population wide-screening tool motivating those with high risk to adjust their lifestyle and visit healthcare providers to control their risk with relevant medications as may be indicated.6,8
Previous studies among stroke survivors and health personnel in the region have shown feasibility and acceptability of mobile health interventions for secondary prevention of stroke.22–24 The profound shortage of doctors, nurses, and health educators in SSA means that stroke risk factor screening and management should be a shared responsibility of community members and non-physician healthcare providers, such as community health workers for measurements of blood pressure, blood glucose and lipids required for complete stroke risk assessment. It is likely that a blend of approaches, such as self-assessment by community members, hospital-based screening, and domiciliary and communal screening by community health workers, and would be needed to implement stroke risk screening in Africa. Digital platforms could be utilized as a viable option in all these approaches. Governmental commitment, however, would be essential for a sustainable primary prevention program given that over 65% of participants opined that cost of screening should be borne by government.
Limitations and Future Directions:
We did not assess knowledge of study participants on specific stroke risk factors, although respondents indicated a high level of awareness of risk factors. The survey was conducted in communities in Nigeria (Ibadan, Abeokuta, and Zaria) that have been involved in ongoing stroke studies and may therefore have been exposed to information on stroke. Due to the cross-sectional design, causal inferences between outcome measures, such as willingness to know individual level stroke risk and willingness to use mobile health for screening, cannot be drawn.
The next steps are to: 1) develop and validate Afrocentric stroke risk prediction models; 2) translate these models into mobile Apps for stroke risk assessment within African communities; 3) sequentially test the effectiveness of such digital interventions in stroke prevention initially using pilot clinical trials followed by larger, more definitive multicenter and transnational clinical trials with assessment of implementation outcome measures. In support of this, we have recently completed a pilot clinical trial among 100 stroke free Ghanaians and Nigerians randomized into usual care with one time counselling (control arm) versus a 2-month educational intervention comprising of a stroke video and riskometer app aimed at improving stroke risk factor awareness and health seeking behavioral modification. We observed a significant reduction in total stroke risk score and the intervention was found to be feasible by study participants and health professionals in our settings.37
Conclusions:
Community dwelling Nigerians expresswanted to know their immediate risk of stroke using digital platforms, such as a stroke risk calculator mobile phone application. Clinical trials are needed to assess the effectiveness of mHealth approaches for primary prevention of stroke in sub-Saharan African communities, which now bear a huge burden of stroke.
Funding:
This study and investigators are supported by the National Institutes of Health NIH/NINDS SIREN (U54HG007479), SIBS Genomics (R01NS107900), and SIBS Gen Gen (R01NS107900-02S1), ARISES (R01NS115944-01), H3Africa CVD Supplement (3U24HG009780-03S5), CaNVAS (1R01NS114045-01), Sub-Saharan Africa Conference on Stroke (SSACS) 1R13NS115395-01A1, Training Africans to Lead and Execute Neurological Trials & Studies (TALENTS) D43TW012030 and ELSI grant 1U01HG010273.
Footnotes
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Disclosures: Nil
Conflict of Interest The authors declare that they have no conflict of interest.
Ethical Approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed Consent Informed consent was obtained from all individual participants included in the study.
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