Temporal Patterns of Engagement and Sentiment in a Suicide Prevention Mobile App: Three-Year Observational Study

AI Summary

Safety planning engagement peaked mid afternoon (14:00 to 15:00) and was lowest overnight, while PRISM-S scores remained stable.
Free-text sentiment was predominantly negative, correlated with greater PRISM-S perceived suffering, and most negative at night, notably around 23:00.
Integrating automated sentiment analysis with time adaptive, personalised interventions could detect heightened distress and deliver timely support, addressing underaddressed night time needs.

AI Summary

Understanding patterns of app engagement, perceived suffering, and affective expression can inform the design of proactive, personalized digital interventions, thereby impacting adherence and efficacy.

OBJECTIVE: This study aimed to examine temporal patterns of engagement with 2 core components of the suicide prevention app SERO (Suicide Prevention: a Uniform Effort, Resource-Oriented; BFH, Lucerne Psychiatry), specifically the safety plan and the PRISM-S (Pictorial Representation of Illness and Self-Measure-Suicidality) self-assessment, using 3 years of interaction log data, assessing variations across circadian, weekly, and seasonal cycles, and evaluating the sentiment of free-text responses submitted immediately after PRISM-S self-assessments.

Separately, 1212 app users accessed the PRISM-S self-assessment, producing 2329 entries (mean distance 12.91, 95% CI 12.39-13.42 cm), with most app users recording only 1 or 2 registrations.

Basic summary

JMIR Ment Health. 2026 Jul 16;13:e95374. doi: 10.2196/95374.

ABSTRACT

BACKGROUND: Temporal fluctuations in distress and suicidal ideation across daily, weekly, and seasonal cycles may influence the use and effectiveness of digital suicide prevention tools. Understanding patterns of app engagement, perceived suffering, and affective expression can inform the design of proactive, personalized digital interventions, thereby impacting adherence and efficacy.

METHODS: We analyzed anonymized interaction logs from the SERO app collected over 3 years (November 2022 to December 2025). Engagement metrics included the frequency of use of the safety planning functionality and PRISM-S self-assessment entries. Free-text responses provided after PRISM-S assessments were analyzed using automated sentiment classification. Temporal analyses examined variations by the hour of the day, day of the week, and season. One-way ANOVAs, post hoc tests, and Pearson correlations were used to examine patterns and associations between perceived suffering and sentiment.

RESULTS: A total of 1076 users engaged with the safety planning functionality of the SERO app, generating 3502 entries, with coping strategies and warning signs showing the highest mean interactions and personal beliefs the lowest. Separately, 1212 app users accessed the PRISM-S self-assessment, producing 2329 entries (mean distance 12.91, 95% CI 12.39-13.42 cm), with most app users recording only 1 or 2 registrations. Safety planning engagement showed clear diurnal patterns, peaking in the afternoon (2 PM to 3 PM) and being lowest at night (midnight to 3 AM), whereas PRISM-S scores were stable across time. Sentiment analysis revealed predominantly negative affect (mean score of -0.41, SD 0.51, 95% CI -0.44 to -0.39), correlated with PRISM-S distance, and was most negative at night (specifically at 11 PM) and during the afternoon (2 PM to 5 PM). Seasonal effects were small but significant for PRISM-S, with the lowest perceived suffering in summer.

CONCLUSIONS: Digital suicide prevention tools can support routine patterns of coping behavior, but periods of increased reported distress, particularly at night, may be underaddressed. Integrating automated sentiment analysis alongside self-assessments could potentially enable personalized, time-adaptive interventions that detect changes in emotional state and deliver timely, tailored support, thereby strengthening proactive engagement and resilience.

PMID:42462271 | DOI:10.2196/95374

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