Factors associated with dropout during recruitment and follow-up periods of a mHealth-based randomized controlled trial for Mobile.Net to encourage treatment adherence for people with serious mental health problems

Background: Clinical trials are the gold standard of evidence-based practice. Still many papers inadequately report methodology in randomized controlled trials (RCTs), particularly for mHealth interventions for people with serious mental health problems. To ensure robust enough evidence, it is important to understand which study phases are the most vulnerable in the field of mental health care. Objective: We mapped the recruitment and the trial follow-up periods of participants to provide a picture of the dropout predictors from a mHealth-based trial. As an example, we used a mHealth-based multicenter RCT, titled “Mobile.Net,” targeted at people with serious mental health problems. Methods: Recruitment and follow-up processes of the Mobile.Net trial were monitored and analyzed. Recruitment outcomes were recorded as screened, eligible, consent not asked, refused, and enrolled. Patient engagement was recorded as follow-up outcomes: (1) attrition during short message service (SMS) text message intervention and (2) attrition during the 12-month follow-up period. Multiple regression analysis was used to identify which demographic factors were related to recruitment and retention. Results: We recruited 1139 patients during a 15-month period. Of 11,530 people screened, 36.31% (n=4186) were eligible. This eligible group tended to be significantly younger (mean 39.2, SD 13.2 years, P<.001) and more often women (2103/4181, 50.30%) than those who were not eligible (age: mean 43.7, SD 14.6 years; women: 3633/6514, 55.78%). At the point when potential participants were asked to give consent, a further 2278 refused. Those who refused were a little older (mean 40.2, SD 13.9 years) than those who agreed to participate (mean 38.3, SD 12.5 years; t1842=3.2, P<.001). We measured the outcomes after 12 months of the SMS text message intervention. Attrition from the SMS text message intervention was 4.8% (27/563). The patient dropout rate after 12 months was 0.36% (4/1123), as discovered from the register data. In all, 3.12% (35/1123) of the participants withdrew from the trial. However, dropout rates from the patient survey (either by paper or telephone interview) were 52.45% (589/1123) and 27.8% (155/558), respectively. Almost all participants (536/563, 95.2%) tolerated the intervention, but those who discontinued were more often women (21/27, 78%; P=.009). Finally, participants’ age (P<.001), gender (P<.001), vocational education (P=.04), and employment status (P<.001) seemed to predict their risk of dropping out from the postal survey. Conclusions: Patient recruitment and engagement in the 12-month follow-up conducted with a postal survey were the most vulnerable phases in the SMS text message-based trial. People with serious mental health problems may need extra support during the recruitment process and in engaging them in SMS text message-based trials to ensure robust enough evidence for mental health care

Short text messages to encourage adherence to medication and follow-up for people with psychosis (Mobile.Net): randomized controlled trial in Finland

Background: A text messaging service (short message service [SMS]) has the potential to target large groups of people with long-term illnesses such as serious mental disorders, who may have difficulty with treatment adherence. Robust research on the impact of mobile technology interventions for these patients remains scarce. Objective: The main objective of our study was to investigate the impact of individually tailored short text messages on the rate of psychiatric hospital readmissions, health care service use, and clinical outcomes. In addition, we analyzed treatment costs. Methods: Between September 2011 and November 2012, we randomly assigned 1139 people to a tailored text message intervention (n=569) or usual care (n=570). Participants received semiautomated text messages for up to 12 months or usual care. The primary outcome, based on routinely collected health register data, was patient readmission into a psychiatric hospital during a 12-month follow-up period. Secondary outcomes were related to other service use, coercion, medication, adverse events, satisfaction, social functioning, quality of life, and economic factors (cost analysis). Results: There was 98.24% (1119/1139) follow-up at 12 months. Tailored mobile telephone text messages did not reduce the rate of hospital admissions (242/563, 43.0% of the SMS group vs 216/556, 38.8% of the control group; relative risk 1.11; 95% CI 0.92-1.33; P=.28), time between hospitalizations (mean difference 7.0 days 95% CI –8.0 to 24.0; P=.37), time spent in a psychiatric hospital during the year (mean difference 2.0 days 95% CI –2.0 to 7.0; P=.35), or other service outcomes. People who received text messages were less disabled, based on Global Assessment Scale scores at the time of their readmission, than those who did not receive text messages (odds ratio 0.68; 95% CI 0.47-0.97; P=.04). The costs of treatment were higher for people in the SMS group than in the control group (mean €10,103 vs €9210, respectively, P<.001). Conclusions: High-grade routinely collected data can provide clear outcomes for pragmatic randomized trials. SMS messaging tailored with the input of each individual patient did not decrease the rate of psychiatric hospital visits after the 12 months of follow-up. Although there may have been other, more subtle effects, the results of these were not evident in outcomes of agreed importance to clinicians, policymakers, and patients and their families