Abstracts

Introduction

The high prevalence of depression in older adults ¹1. Vaughan L, Corbin AL, Goveas JS. Depression and frailty in later life: a systematic review. Clin Interv Aging 2015; 10:1947-58. and unequal access to treatment leads to challenges in mental health service delivery ²2. Sivakumar PT, Mukku SSR, Kar N, Manjunatha N, Phutane VH, Sinha P, et al. Geriatric telepsychiatry: promoting access to geriatric mental health care beyond the physical barriers. Indian J Psychol Med 2020; 42(5 Suppl): 41S-6.. The COVID-19 pandemic has created massive disruptions to in-person healthcare worldwide, compromising the treatments for the most vulnerable individuals. An unintended outcome of this is the opportunities of telehealth ³3. Blandford A, Wesson J, Amalberti R, AlHazme R, Allwihan R. Opportunities and challenges for telehealth within, and beyond, a pandemic. Lancet Glob Health 2020; 8:e1364-5..

Interactive Health Communication Applications (IHCA) allow individuals to use an electronic device to access or transmit health information or receive guidance. IHCA seem to have positive effects on behavioral and clinical outcomes of chronic patients ⁴4. Murray E, Burns J, See TS, Lai R, Nazareth I. Interactive health communication applications for people with chronic disease. Cochrane Database Syst Rev2005; (4):CD004274.. The use of WhatsApp as a psycho-educational resource for older people with depression also seems to be a promising strategy ⁵5. Jarvis MA, Anita P, Jennifer C. An evaluation of a low-intensity cognitive behavioral therapy mHealth-supported intervention to reduce loneliness in older people. Int J Environ Res Public Health 2019; 16:1305.. However, further research is needed to determine the most feasible way to deliver IHCA among different groups of patients ⁴4. Murray E, Burns J, See TS, Lai R, Nazareth I. Interactive health communication applications for people with chronic disease. Cochrane Database Syst Rev2005; (4):CD004274., and to identify older adults who are most likely to be benefited by these programs ⁶6. Chen YR, Peter JS. The effect of information communication technology interventions on reducing social isolation in the elderly: a systematic review. J Med Internet Res 2016; 18:e18..

WhatsApp use is frequent among Brazilians ⁷7. Trude ACB, Martins RC, Martins-Silva T. A WhatsApp-based intervention to improve maternal social support and maternal-child health in Southern Brazil: the text-message intervention to enhance social support (TIES) feasibility study. Inquiry 2021; 58:469580211048701., and it is being increasingly used as a health communication tool in digital mental health programs ⁴4. Murray E, Burns J, See TS, Lai R, Nazareth I. Interactive health communication applications for people with chronic disease. Cochrane Database Syst Rev2005; (4):CD004274.^,77. Trude ACB, Martins RC, Martins-Silva T. A WhatsApp-based intervention to improve maternal social support and maternal-child health in Southern Brazil: the text-message intervention to enhance social support (TIES) feasibility study. Inquiry 2021; 58:469580211048701.. The widespread of mobile phones in low- and middle-income countries (LMICs) has created an incentive for the use of WhatsApp in primary health care ⁸8. Henry JV, Winters N, Lakati A, Oliver M, Geniets A, Mbae SM, et al. Enhancing the supervision of community health workers with WhatsApp mobile messaging: qualitative findings from 2 low-resource settings in Kenya. Glob Health Sci Pract 2016; 4:311-25.. Thus, a better knowledge of sociodemographic characteristics that may prevent older adults with or without depression to use WhatsApp is needed, as well as the use of mobile phones as a crucial tool to offer better communication strategies to this audience.

This study aimed to investigate the relationship between sociodemographic characteristics, depressive symptomatology, mobile phone ownership, and different uses of WhatsApp among older adults enrolled in primary care clinics in Guarulhos, São Paulo State, Brazil.

Methods

Sample and procedure

This is a secondary data analysis using cross-sectional data collected during the recruitment phase of the PROACTIVE cluster randomized controlled trial ⁹9. Scazufca M, Nakamura CA, Peters TJ, Henrique MG, Seabra A, La Rotta EG, et al. A collaborative care psychosocial intervention to improve late life depression in socioeconomically deprived areas of Guarulhos, Brazil: the PROACTIVE cluster randomised controlled trial protocol. Trials 2020; 21:1206.. The study was approved by the Research Ethics Committee of the School of Medicine, University of São Paulo (n. 2,836,569).

Recruitment and data collection occurred from May 2019 to February 2020 with individuals aged ≥ 60 years registered in 20 primary care clinics in Guarulhos. Participants provided verbal or written consent. The Guarulhos Municipal Health Department provided a list with the contacts of all older adults registered in these clinics (n = 37,218). The names in this list underwent a process of pre-check for eligibility, excluding outdated information, duplicated names, wrong age in database, and inconsistent information. After the pre-check for eligibility, the list was randomly ordered among a previously trained team of independent research assistants that were able to establish a first contact by phone or at home with 8,147 people, number sufficient to reach the sample size calculated for the clinical trial. Then, 4,791 were excluded due to problems including non-valid phone number, person not found, exclusion criteria, refusal, and dead individuals. Thus, 3,356 individuals were screened for depression by research assistants ⁹9. Scazufca M, Nakamura CA, Peters TJ, Henrique MG, Seabra A, La Rotta EG, et al. A collaborative care psychosocial intervention to improve late life depression in socioeconomically deprived areas of Guarulhos, Brazil: the PROACTIVE cluster randomised controlled trial protocol. Trials 2020; 21:1206..

Measures

The study used the Patient Health Questionnaire-9 (PHQ-9) ¹⁰10. Levis B, Benedetti A, Thombs BD; DEPRESsion Screening Data (DEPRESSD) Collaboration. Accuracy of Patient Health Questionnaire‐9 (PHQ‐9) for screening to detect major depression: individual participant data meta‐analysis. BMJ 2019; 365:l1476. to assess depression, and a cut-off point of 10+ indicated depressive symptomatology, a threshold that has been reported to provide a 91% sensitivity and 88%specificity among individuals aged ≥ 60 years ¹⁰10. Levis B, Benedetti A, Thombs BD; DEPRESsion Screening Data (DEPRESSD) Collaboration. Accuracy of Patient Health Questionnaire‐9 (PHQ‐9) for screening to detect major depression: individual participant data meta‐analysis. BMJ 2019; 365:l1476.. Information on mobile phone ownership and the use of WhatsApp was evaluated through the following questions: “Do you have an own mobile phone?”, “Do you send or receive text or audio messages by WhatsApp?”, and “Do you watch videos that you receive by WhatsApp?”. Personal income was assessed based on Brazilian minimum wage in 2019.

Statistical analyses

To explore sociodemographic and socioeconomic characteristics associated with the outcome measures (mobile phone ownership and WhatsApp use), multiple logistic regression models were used. Sociodemographic and socioeconomic characteristics collected as part of the pre-registered trial were included as predictors in the models, including age group (60-69, 70-79, and ≥ 80 years), gender, personal income (≤ 1, 1-2, 2-3, and > 3 minimum wages), and schooling level in years (none, 1-4, 5-8, and ≥ 8). Models estimated in the overall sample also included whether the participant had depressive symptomatology above the threshold. The same set of models were estimated in the depressed subsample (PHQ-9 ≥ 10).

The variance inflation factor (VIF) of the predictors were calculated to assess the existence of multicollinearity. None of the VIFs were greater than ten, suggesting that multicollinearity was not a problem.

Data management and analyses were performed in Stata/MP 16.1 (https://www.stata.com).

Results

Among the 3,356 individuals screened for depression, 1,020 presented depressive symptomatology. Table 1 shows the characteristics of both samples.

Tables 2 and 3 show the results of the multiple logistic regression models. In the overall sample, non-depressive, younger, wealthier, and with more years of schooling older adults were more likely to possess a mobile phone (Table 2, upper section) and to use WhatsApp for messaging or to watch received videos (Table 3, upper section). Mobile phone ownership was similar for women and men, but women were more likely than men to use WhatsApp for sending and receiving messages as well as for watching videos.

Results with the subsample of participants with depressive symptomatology (Table 2 and 3, lower section) did not differ significantly from those with the overall sample, with the exception that, in the depressed subsample, women were more likely than men to possess a mobile phone. In the subsample, older adults with a personal income above 3 minimum wages had higher odds of having a mobile phone (odds ratio - OR = 4.40; 95% confidence interval - 95%CI: 1.57; 12.31), use WhatsApp for messaging (OR = 4.96; 95%CI: 2.08; 11.84), or watch videos received via WhatsApp (OR = 4.99; 95%CI: 2.12; 11.72), than their counterparts with a personal income up to 1 minimum wage. Regarding schooling years, older adults with more than eight years of schooling had higher odds of having a mobile phone than their counterparts with no education (OR = 2.96; 95%CI: 1.65; 5.31), with even larger odds for using WhatsApp for messaging (OR = 8.30; 95%CI: 4.48; 15.38) and watching videos (OR = 6.21; 95%CI: 3.39; 11.38). Regarding age, oldest adults (≥ 80 years old) had lower odds of having a mobile phone (OR = 0.22; 95%CI: 0.13; 0.38), of using WhatsApp for messaging (OR = 0.17; 95%CI: 0.08; 0.35), and of watching videos received via WhatsApp (OR = 0.12; 95%CI: 0.05; 0.28) than their youngest (60-69 years old) counterparts. Finally, women were more likely to have a mobile phone (OR = 1.59; 95%CI: 1.15; 2.20), use WhatsApp for messaging (OR = 2.65; 95%CI: 1.58; 3.80) and watching received videos (OR = 2.36; 95%CI: 1.64; 3.41) than men.

Discussion

WhatsApp is a widely used application software in the sample, however we found systematic differences in the mobile phone ownership and WhatsApp use among older adults in both samples. In both the overall sample and depressed subsample, lower schooling years, lower income, and higher age were associated with lower odds of having a mobile phone and using WhatsApp. These differences may interfere with equitable access to mental health treatments delivered via IHCAs such as WhatsApp. Women were also more likely to have a mobile phone and to use WhatsApp than men, hence this should be considered when implementing telehealth programs.

In the literature, age, education, and income are inversely associated with internet access and use of the internet for telehealth ¹¹11. Matthews K, Nazroo J, Marshall A. Digital inclusion in later life: cohort changes in internet use over a ten-year period in England. Ageing Soc 2019; 39:1914-32.^,1212. Kunonga TP, Spiers GF, Beyer F, Hanratty B, Boulton E, Hall A, et al. Effects of digital technologies on older people’s access to health and social care: umbrella review. J Med Internet Res 2021; 23:e25887.. In this sense, research should be conducted in different contexts and cultures, focusing on identifying the groups most vulnerable to exclusion from telehealth-based interventions to address these inequalities ¹²12. Kunonga TP, Spiers GF, Beyer F, Hanratty B, Boulton E, Hall A, et al. Effects of digital technologies on older people’s access to health and social care: umbrella review. J Med Internet Res 2021; 23:e25887..

This study has limitations. First, the research was conducted with older adults registered in primary care clinics in Guarulhos, so it may not be generalizable to other cities in Brazil. Second, we collected information on personal income, which may not capture people’s economic situation as adequately as family or household income measures. Due to the cross‐sectional design of our study, no directionality can be inferred from these results. Also, as a secondary data analysis, we could only consider the sociodemographic and socioeconomic variables included in the original protocol. On the other hand, this study provided information on a socioeconomically deprived population within an upper-middle-income economy, which may help closing the treatment gap in the future, especially for the most vulnerable populations.

We investigated a population with approximately 60% of people with low level of formal education and personal income. Even in this context, being in the most disadvantaged group was negatively associated with mobile phone ownership and use of WhatsApp. Furthermore, individuals identified with depressive symptomatology were less likely to possess a mobile phone and use WhatsApp compared to the overall sample.

This study concludes that gender, age, schooling level, income, and depressive symptomatology are variables that are associated with the possession of a cell phone and with the use of WhatsApp by the studied older adults. Further studies on this topic are needed to empower these preliminary findings and enlarge it to a broader population.

These findings can help to implement digital health programs better suited to disadvantaged populations in Brazil and other LMIC, especially mental telehealth interventions using WhatsApp and mobile health services for older adults.

Acknowledgments

To São Paulo State Research Foundation (FAPESP; process n. 2017/50094‐2), U.K. Medical Research Council (MRC; process n. MR/R006229/1). The authors would like to thank the Guarulhos Municipal Health Department for the support provided. Individual acknowledgments: F. A. Moretti (FAPESP: 20/02272-1); C. A. Nakamura (FAPESP: 2018/19343-0); M. Scazufca (Brazilian National Research Council - CNPq: 307579/2-19-0); C. H. Q. Souza (FAPESP: 20/14504-4); D. Moreno-Agostino (Economic and Social Research Council - ERSC, Centre for Society and Mental Health at King’s College London; ES/S012567/1). The views expressed are those of the authors and not necessarily those of the ESRC, MRC or King’s College London.

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