A mobile healthy lifestyle intervention to promote mental health in adolescence: a mixed-methods evaluation.

BACKGROUND: A healthy lifestyle may improve mental health. It is yet not known whether and how a mobile intervention can be of help in achieving this in adolescents. This study investigated the effectiveness and perceived underlying mechanisms of the mobile health (mHealth) intervention #LIFEGOALS to promote healthy lifestyles and mental health. #LIFEGOALS is an evidence-based app with activity tracker, including self-regulation techniques, gamification elements, a support chatbot, and health narrative videos. METHODS: A quasi-randomized controlled trial (N = 279) with 12-week intervention period and process evaluation interviews (n = 13) took place during the COVID-19 pandemic. Adolescents (12-15y) from the general population were allocated at school-level to the intervention (n = 184) or to a no-intervention group (n = 95). Health-related quality of life (HRQoL), psychological well-being, mood, self-perception, peer support, resilience, depressed feelings, sleep quality and breakfast frequency were assessed via a web-based survey; physical activity, sedentary time, and sleep routine via Axivity accelerometers. Multilevel generalized linear models were fitted to investigate intervention effects and moderation by pandemic-related measures. Interviews were coded using thematic analysis. RESULTS: Non-usage attrition was high: 18% of the participants in the intervention group never used the app. An additional 30% stopped usage by the second week. Beneficial intervention effects were found for physical activity (χ21 = 4.36, P = .04), sedentary behavior (χ21 = 6.44, P = .01), sleep quality (χ21 = 6.11, P = .01), and mood (χ21 = 2.30, P = .02). However, effects on activity-related behavior were only present for adolescents having normal sports access, and effects on mood only for adolescents with full in-school education. HRQoL (χ22 = 14.72, P < .001), mood (χ21 = 6.03, P = .01), and peer support (χ21 = 13.69, P < .001) worsened in adolescents with pandemic-induced remote-education. Interviewees reported that the reward system, self-regulation guidance, and increased health awareness had contributed to their behavior change. They also pointed to the importance of social factors, quality of technology and autonomy for mHealth effectiveness. CONCLUSIONS: #LIFEGOALS showed mixed results on health behaviors and mental health. The findings highlight the role of contextual factors for mHealth promotion in adolescence, and provide suggestions to optimize support by a chatbot and narrative episodes. TRIAL REGISTRATION: ClinicalTrials.gov [NCT04719858], registered on 22/01/2021.

Nonusage Attrition of Adolescents in an mHealth Promotion Intervention and the Role of Socioeconomic Status: Secondary Analysis of a 2-Arm Cluster-Controlled Trial.

BACKGROUND: Mobile health (mHealth) interventions may help adolescents adopt healthy lifestyles. However, attrition in these interventions is high. Overall, there is a lack of research on nonusage attrition in adolescents, particularly regarding the role of socioeconomic status (SES). OBJECTIVE: The aim of this study was to focus on the role of SES in the following three research questions (RQs): When do adolescents stop using an mHealth intervention (RQ1)? Why do they report nonusage attrition (RQ2)? Which intervention components (ie, self-regulation component, narrative, and chatbot) prevent nonusage attrition among adolescents (RQ3)? METHODS: A total of 186 Flemish adolescents (aged 12-15 years) participated in a 12-week mHealth program. Log data were monitored to measure nonusage attrition and usage duration for the 3 intervention components. A web-based questionnaire was administered to assess reasons for attrition. A survival analysis was conducted to estimate the time to attrition and determine whether this differed according to SES (RQ1). Descriptive statistics were performed to map the attrition reasons, and Fisher exact tests were used to determine if these reasons differed depending on the educational track (RQ2). Mixed effects Cox proportional hazard regression models were used to estimate the associations between the use duration of the 3 components during the first week and attrition. An interaction term was added to the regression models to determine whether associations differed by the educational track (RQ3). RESULTS: After 12 weeks, 95.7% (178/186) of the participants stopped using the app. 30.1% (56/186) of the adolescents only opened the app on the installation day, and 44.1% (82/186) stopped using the app in the first week. Attrition at any given time during the intervention period was higher for adolescents from the nonacademic educational track compared with those from the academic track. The other SES indicators (family affluence and perceived financial situation) did not explain attrition. The most common reasons for nonusage attrition among participants were perceiving that the app did not lead to behavior change, not liking the app, thinking that they already had a sufficiently healthy lifestyle, using other apps, and not being motivated by the environment. Attrition reasons did not differ depending on the educational track. More time spent in the self-regulation and narrative components during the first week was associated with lower attrition, whereas chatbot use duration was not associated with attrition rates. No moderating effects of SES were observed in the latter association. CONCLUSIONS: Nonusage attrition was high, especially among adolescents in the nonacademic educational track. The reported reasons for attrition were diverse, with no statistical differences according to the educational level. The duration of the use of the self-regulation and narrative components during the first week may prevent attrition for both educational tracks. TRIAL REGISTRATION: ClinicalTrials.gov NCT04719858; http://clinicaltrials.gov/ct2/show/NCT04719858.

Assessing sleep-related attitudes with the implicit association test: A prospective study in young adults.

The measurement of automatic attitudes towards sleep, in addition to reflective self-reports, might improve our ability to predict and explain sleep-hindering practices. Two types of implicit association tests (IATs), a sleep-related evaluations IAT and a sleep-related self-identity IAT, were developed to evaluate their efficacy for assessing automatic sleep-related attitudes. In addition, a speeded self-report measure of sleep evaluations was explored as a means to assess automatic sleep-related attitudes. The study included 136 young adults (age = 21.70 ± 2.22, 43% female). At baseline, the two IATs, the speeded self-report, and standard self-reports of sleep determinants (reflective attitudes, self-efficacy, intention and action planning for sleep-promoting behaviour), sleep hygiene practices, sleep quality, and sleep duration were assessed. All variables except for the sleep determinants were assessed again at 2-week follow-up. The results demonstrated good reliability of the two IAT versions, but both IATs were unrelated to the speeded self-report, the sleep determinants, sleep practices, sleep quality or sleep duration. The speeded self-report correlated significantly with the standard self-reports of sleep determinants. Baseline scores on the IATs or speeded self-report did not predict sleep hygiene practices, sleep duration or sleep quality at follow-up. The findings indicate that sleep-related IATs might not be suited to assess automatic sleep-related attitudes. Further investigation is needed to determine whether speeded self-reports are valid measures of automatic attitudes. Moreover, more empirical research is required to clarify the role of automatic processes for sleep hygiene behaviours.

Participatory Development and Pilot Testing of an Adolescent Health Promotion Chatbot.

Background: The use of chatbots may increase engagement with digital behavior change interventions in youth by providing human-like interaction. Following a Person-Based Approach (PBA), integrating user preferences in digital tool development is crucial for engagement, whereas information on youth preferences for health chatbots is currently limited. Objective: The aim of this study was to gain an in-depth understanding of adolescents' expectations and preferences for health chatbots and describe the systematic development of a health promotion chatbot. Methods: Three studies in three different stages of PBA were conducted: (1) a qualitative focus group study (n = 36), (2) log data analysis during pretesting (n = 6), and (3) a mixed-method pilot testing (n = 73). Results: Confidentiality, connection to youth culture, and preferences when referring to other sources were important aspects for youth in chatbots. Youth also wanted a chatbot to provide small talk and broader support (e.g., technical support with the tool) rather than specifically in relation to health behaviors. Despite the meticulous approach of PBA, user engagement with the developed chatbot was modest. Conclusion: This study highlights that conducting formative research at different stages is an added value and that adolescents have different chatbot preferences than adults. Further improvement to build an engaging chatbot for youth may stem from using living databases.

Sequential effects of reappraisal and rumination on anger during recall of an anger-provoking event.

In everyday life, people often combine strategies to regulate their emotions. However, to date, most research has investigated emotion regulation strategies as if they occur independently from one another. The current study aims to better understand the sequential interplay between strategies by investigating how reappraisal and rumination interact to affect anger experience. After participants (N = 156) recalled a recent anger-provoking event, they were instructed to either a) reappraise the event twice, b) reappraise the event, and then ruminate about the event, c) ruminate about the event, and then reappraise the event, or d) ruminate twice about the event. The effects of the first strategy used replicated a large body of research: reappraisal was associated with a decrease in anger, but rumination was associated with no change in anger. There was a small interactive effect of the combination of the two strategies, such that those who ruminated and then reappraised showed a larger decrease in anger than those who reappraised and then ruminated. There were no other differences between groups. This suggests that the second strategy does have an effect over and beyond the first strategy, but this effect is small in size, highlighting the importance of the initial emotion regulation strategy used.

Diagnostic accuracy and treatment approach to depression in primary care: predictive factors.

Objective: The study assessed the predictive factors of diagnostic accuracy and treatment approach (antidepressants versus active monitoring) for depression in primary care. Methods: This is a cross-sectional study that uses information from a naturalistic prospective controlled trial performed in Barcelona (Spain) enrolling newly diagnosed patients with mild to moderate depression by GPs. Treatment approach was based on clinical judgement. Diagnosis was later assessed according to DSM-IV criteria using Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) interview by an external researcher. Patients (sociodemographic, psychiatric diagnosis, severity of depression and anxiety, health-related quality of life, disability, beliefs about medication and illness and comorbidities) and GP factors associated with diagnostic accuracy and treatment approach were assessed using multilevel logistic regression. Variables with missing data were imputed through multiple imputations. Results: Two hundred sixty-three patients were recruited by 53 GPs. Mean age was 51 years (SD = 15). Thirty percent met DSM-IV criteria for major depression. Mean depression symptomatology was moderate-severe. Using multivariate analyses, patients' beliefs about medicines were the only variable associated with the antidepressant approach. Specialization in general medicine and being a resident tutor were associated with a more accurate diagnosis. Conclusions: Clinical depression diagnosis by GPs was not always associated with a formal diagnosis through a SCID-I. GPs' training background was central to an adequate depression diagnosis. Patients' beliefs in medication were the only factor associated with treatment approach. More resources should be allocated to improving the diagnosis of depression.