Search

Supplementary Material 1

Additional file 1.

Additional file 1. EHEALTH CONSORT extension checklist.

Additional file 3. Consolidated criteria for reporting qualitative studies (COREQ).

mHealth has been proposed to address inefficiencies in the current South African healthcare system, including home-based HIV testing and counseling (HTC) programs. Yet wide-scale adoption of mHealth has not occurred. Even as infrastructure barriers decrease, a need to better understand perceived adoption barriers by stakeholders remains. We conducted focus group discussions (FGD) in South Africa in 2016 with 10 home-based HTC field staff, 12 community health workers (CHWs) and 10 persons living with HIV (PLH). Key informant (KI) interviews were conducted with five health officials. Perceptions about current home-based HTC practices, future mHealth systems and the use of biometrics for patient identification were discussed, recorded and transcribed for qualitative analysis. Themes were based on a conceptual model for perceived mHealth service quality. Stakeholders brought up a lack of communication in sharing patient health information between clinics, between clinics and CHWs, and between clinics and patients as major barriers to care that mHealth can address. CHWs need better patient information from clinics in terms of physical location and health status to plan visitation routes and address patient needs. CHWs perceive that communication barriers create distrust towards them by clinic staff. PLH want automated appointment and medication reminders. KI see mHealth as a way to improve health information transfer to government officials to better allocate healthcare resources. Stakeholders are also optimistic about the ability for biometrics to improve patient identification but disagreed as to which biometrics would be acceptable, especially in older patients. All stakeholders provided useful information towards the development of mHealth systems. Hospitals are adopting patient-centered approaches that solicit feedback from patients and incorporate them into decision-making processes. A similar approach is needed in the development of mHealth systems. Further, such systems are critical to the successful extension of the health system from health facilities into people's homes.

Socio-economic status (SES) is an important predictor of obesity, but how it is associated with differences in physical activity and sedentary behaviour is less clear. This cross-sectional study examined the association between SES (sum of household assets), physical activity and sedentary time, and how they predict adiposity. Socio-demographic, anthropometric, and physical activity data on rural (n = 509) and urban (n = 510) South African women (18-23 years) were collected. Overweight and obesity prevalence, and sedentary time, were higher; and moderate-vigorous intensity physical activity (MVPA) was lower, in the urban sample. Structural equation models (SEMs) were constructed for BMI and waist circumference. In the urban sample SES had a direct inverse effect on MVPA (beta; 95% CI, -41.69; -73.40 to -9.98), while in the rural sample SES had a direct effect on BMI (beta; 95% CI, 0.306; 0.03 to 0.59). In the pooled sample, SES had a direct inverse effect on MVPA (beta; 95% CI, 144; -170.34 to -119.04), and MVPA was directly associated with BMI (beta; 95% CI, 0.04; 0.01 to 0.08). The influence of SES, and the role of physical activity and sedentary time on adiposity differs between the urban and rural samples, and the importance of other environmental and behavioural factors must be considered in the development of obesity and the design of effective interventions.