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Technology has the potential to increase social connectedness among older adults, but one-third do not use the internet. We formed a community partnership, Tech Allies, providing tablets, broadband, and 1:1 training to isolated older adults. In a pragmatic pilot trial, participants were randomized into intervention ( n = 44) and waitlist ( n = 39) groups. Volunteers provided eight weekly, in-home iPad lessons. Surveys assessed self-reported loneliness, social support, technology use, and confidence at baseline and follow-up. A subgroup completed in-home interviews. The intervention group showed no change in loneliness, marginally significant improvement in social support and technology confidence, and significant increase in technology use. Among the waitlist group, no changes were observed. Interviews showed some participants felt more connected to the world, and many expressed increased technology confidence. Key implementation lessons on program feasibility are discussed. Embedding training within existing community-based programs holds promise as a potentially sustainable mechanism to provide digital training to older adults.

Background Health care systems are rapidly deploying digital tools for disease management; however, few studies have evaluated their usability by vulnerable populations. To understand the barriers to app usage among vulnerable populations, we employed user-centered design (UCD) methods in the development of a new text messaging app. Objective The study aimed to describe variations in patients’ engagement in the app design process, focusing on limited health literacy (LHL), limited English proficiency (LEP), and limited digital literacy (LDL). Methods We conducted 20 in-depth semistructured interviews with primary care patients at a public health care system, used open-ended discussions and card sorting tasks to seek input about mobile phones and text messaging, and used open coding to categorize the patterns of mobile phone usage and to evaluate engagement in the card sorting process. We examined qualitative differences in engagement by examining the extensiveness of participant feedback on existing and novel text messaging content and calculated the proportion of patients providing extensive feedback on existing and novel content, overall and by health literacy, English proficiency, and digital literacy. Results The average age of the 20 participants was 59 (SD 8) years; 13 (65%) were female, 18 (90%) were nonwhite, 16 (80%) had LHL, and 13 (65%) had LEP. All had depression, and 14 (70%) had diabetes. Most participants had smartphones (18/20, 90%) and regularly used text messaging (15/20, 75%), but 14 (70%) of them reported having difficulty texting because of inability to type, physical disability, and low literacy. We identified 10 participants as specifically having LDL; 7 of these participants had LEP, and all 10 had LHL. Half of the participants required a modification of the card sorting activity owing to not understanding it or not being able to read the cards in the allotted time. The proportion of participants who gave extensive feedback on existing content was lower in participants with limited versus adequate English proficiency (4/13, 30% vs 5/7, 71%), limited versus adequate health literacy (7/16, 44% vs 3/4, 75%), and limited versus adequate digital literacy (4/10, 40% vs 6/10, 60%); none of these differences were statistically significant. When examining the proportion of patients who gave extensive feedback for novel messaging content, those with LHL were less engaged than those with adequate health literacy (8/16, 50% vs 4/4, 100%); there were no statistical differences by any subgroup. Conclusions Despite widespread mobile phone use, digital literacy barriers are common among vulnerable populations. Engagement in the card sorting activity varied among participants and appeared to be lower among those with LHL, LEP, and LDL. Researchers employing traditional UCD methods should routinely measure these communication domains among their end-user samples. Future work is needed to replicate our findings in larger samples, but augmentation of card sorting with direct observation and audiovisual cues may be more productive in eliciting feedback for those with communication barriers.

IntroductionDepression and diabetes are highly disabling diseases with a high prevalence and high rate of comorbidity, particularly in low-income ethnic minority patients. Though comorbidity increases the risk of adverse outcomes and mortality, most clinical interventions target these diseases separately. Increasing physical activity might be effective to simultaneously lower depressive symptoms and improve glycaemic control. Self-management apps are a cost-effective, scalable and easy access treatment to increase physical activity. However, cutting-edge technological applications often do not reach vulnerable populations and are not tailored to an individual’s behaviour and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention.Methods and analysisIn a three-arm randomised controlled trial, we will examine the effect of a text-messaging smartphone application to encourage physical activity in low-income ethnic minority patients with comorbid diabetes and depression. The adaptive intervention group receives messages chosen from different messaging banks by a reinforcement learning algorithm. The uniform random intervention group receives the same messages, but chosen from the messaging banks with equal probabilities. The control group receives a weekly mood message. We aim to recruit 276 adults from primary care clinics aged 18–75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (Patient Health Questionnaire depression scale-8 (PHQ-8) >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent haemoglobin A1c from medical records at baseline and at intervention completion at 6-month follow-up.Ethics and disseminationThe Institutional Review Board at the University of California San Francisco approved this study (IRB: 17-22608). We plan to submit manuscripts describing our user-designed methods and testing of the adaptive learning algorithm and will submit the results of the trial for publication in peer-reviewed journals and presentations at (inter)-national scientific meetings.Trial registration numberNCT03490253; pre-results.

Objectives Text-messaging interventions are a promising approach to increasing physical activity in vulnerable populations. To better inform the development of a text-messaging intervention, we sought to identify barriers and facilitators to using text messaging and engaging in physical activity among patients with diabetes and comorbid depression. Materials and Methods We conducted interviews with primary care patients at a safety-net health care system (N = 26). Data were collected at 3 stages, including a focus group (stage 1), and individual interviews (stage 2 and 3). Patients in stage 1 and 2 previously participated in a text-messaging intervention as part of depression treatment. Discussions focused on participant experience of previously using a text-messaging intervention, influences and perceptions of physical activity, and mobile phone use. We analyzed all transcripts for emerging themes. Results Participants were 56.2 years (±9.7); 69.2% were female, 65.4% identified as Hispanic/Latino(a), and 46.2% reported having less than a high school education. All had depression and 61.5% had diabetes. Specific barriers that emerged included low literacy and only basic use of mobile phones in everyday life, in combination with a high prevalence of comorbid health conditions and limited mobility. These were each addressed with a specific content or intervention delivery change in the overall intervention design. Conclusions Conducting a focus group and individual interviews with end users of an mHealth intervention under development has implications for tailoring and modifying components of the content and format to ensure that the final intervention will engage end users most effectively.