Larissa Jennings Mayo-Wilson1, Bee-Ah Kang2, Muthoni Mathai3, Margaret O Mak'anyengo4, Fred M Ssewamala5. 1. Indiana University School of Public Health, Department of Applied Health Sciences, 1025 E. 7th Street, Bloomington, IN 47405, USA; Johns Hopkins University, Bloomberg School of Public Health, Department of International Health, 615 N. Wolfe Street, Baltimore, MD, USA. Electronic address: ljmayowi@iu.edu. 2. Johns Hopkins University, Bloomberg School of Public Health, Department of International Health, 615 N. Wolfe Street, Baltimore, MD, USA. 3. University of Nairobi, College of Health Sciences, Department of Psychiatry. Kenyatta National Hospital, Off-Ngong Road, Nairobi, Kenya; National Health and Development Organization (NAHEDO), Kenyatta National Hospital, Department of Mental Health, Ralph Bunche Road, P.O. Box 20453 Nairobi, Kenya. 4. National Health and Development Organization (NAHEDO), Kenyatta National Hospital, Department of Mental Health, Ralph Bunche Road, P.O. Box 20453 Nairobi, Kenya. 5. Washington University in St. Louis, The Brown School, Goldfarb, Room 235, Campus Box 1196, One Brookings, Drive, St. Louis, MO 63130, USA. Electronic address: fms1@wustl.edu.
Abstract
BACKGROUND: Mobile phone-based health (mHealth) interventions have the potential to improve HIV outcomes for high-risk young adults living in informal urban settlements in Kenya. However, less is known regarding young adults' differential access to mobile phones and their willingness and use of mobile phone technologies to access HIV prevention, care, and treatment services. This is important as young adults make up the largest demographic segment of impoverished, informal urban settlements and are disproportionately impacted by HIV. METHODS: This study used observational survey data from 350 young adults, aged 18-22, who were living informal urban settlements in Nairobi, Kenya. Respondent driven sampling methods were used to recruit and enroll eligible youth. Using descriptive statistics and logistical regressions, we examined the prevalence of mobile phone access, willingness, and use for HIV services. We also assessed associated demographic characteristics in the odds of access, willingness, and use. RESULTS: The mean age of participants was 19 years (±1.3). 56% were male. Mobile phone coverage, including text messaging and mobile internet, was high (>80%), but only 15% of young adults had ever used mobile phones to access HIV services. Willingness was high (65%), especially among those who had individual phone access (77%) compared to lower willingness (18%) among those who shared a phone. More educated (OR = 1.84, 95 %CI:1.14-2.97) and employed (OR = 1.70, 95 %CI:1.02 = 2.83) young adults were also more willing to use phones for HIV services. In contrast, participants living in large households (OR = 0.47, 95 %CI:0.24-0.921), were religious minorities (OR = 0.56, 95 %CI:0.32-0.99), partnered/married (OR = 0.30, 95 %CI:0.10-0.91), or female (OR = 0.29, 95 %CI:0.16-0.55) were significantly less likely to have mobile phone access or usage, limiting their potential participation in HIV-related mHealth interventions. Given the low usage of mobile phones currently for HIV services, no differences in demographic characteristics were observed. CONCLUSION: Mobile health technologies may be under-utilized in HIV services for at-risk youth. Our findings highlight the importance of preliminary, formative research regarding population differences in access, willingness, and use of mobile phones for HIV services. More efforts are needed to ensure that mHealth interventions account for potential differences in preferences for mobile phone-based HIV interventions by gender, age, religion, education, and/or employment status.
BACKGROUND: Mobile phone-based health (mHealth) interventions have the potential to improve HIV outcomes for high-risk young adults living in informal urban settlements in Kenya. However, less is known regarding young adults' differential access to mobile phones and their willingness and use of mobile phone technologies to access HIV prevention, care, and treatment services. This is important as young adults make up the largest demographic segment of impoverished, informal urban settlements and are disproportionately impacted by HIV. METHODS: This study used observational survey data from 350 young adults, aged 18-22, who were living informal urban settlements in Nairobi, Kenya. Respondent driven sampling methods were used to recruit and enroll eligible youth. Using descriptive statistics and logistical regressions, we examined the prevalence of mobile phone access, willingness, and use for HIV services. We also assessed associated demographic characteristics in the odds of access, willingness, and use. RESULTS: The mean age of participants was 19 years (±1.3). 56% were male. Mobile phone coverage, including text messaging and mobile internet, was high (>80%), but only 15% of young adults had ever used mobile phones to access HIV services. Willingness was high (65%), especially among those who had individual phone access (77%) compared to lower willingness (18%) among those who shared a phone. More educated (OR = 1.84, 95 %CI:1.14-2.97) and employed (OR = 1.70, 95 %CI:1.02 = 2.83) young adults were also more willing to use phones for HIV services. In contrast, participants living in large households (OR = 0.47, 95 %CI:0.24-0.921), were religious minorities (OR = 0.56, 95 %CI:0.32-0.99), partnered/married (OR = 0.30, 95 %CI:0.10-0.91), or female (OR = 0.29, 95 %CI:0.16-0.55) were significantly less likely to have mobile phone access or usage, limiting their potential participation in HIV-related mHealth interventions. Given the low usage of mobile phones currently for HIV services, no differences in demographic characteristics were observed. CONCLUSION: Mobile health technologies may be under-utilized in HIV services for at-risk youth. Our findings highlight the importance of preliminary, formative research regarding population differences in access, willingness, and use of mobile phones for HIV services. More efforts are needed to ensure that mHealth interventions account for potential differences in preferences for mobile phone-based HIV interventions by gender, age, religion, education, and/or employment status.
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