1. Home
  2. Browse
  3. Smartphones as mood barometers: Pr…
Preregistration

Smartphones as mood barometers: Predicting mood in daily life using different sensing modalities

Author(s) / Creator(s)

Kunz, Fiona

Abstract / Description

Momentary experiences of positive and negative emotionality are core components of well-being and performance. This study investigates whether passively sensed smartphone data can be used to recognize individuals’ mood (i.e. Valence and Arousal (Russell, 1980)) based on their smartphone sensing data. The exploratory analysis uses data generated from N = 453 participants in a two-week experience sampling wave which was part of the Smartphone Sensing Panel Study (SSPS; Schödel & Oldemeier, 2020). Different cross-validated machine learning algorithms are compared to predict participants’ current mood given a variety of situational and behavioral variables, reflected by different smartphone sensing modalities. Moreover, the impact of different time perspectives (i.e. daily versus hourly) on the predictive performance is investigated.

Keyword(s)

Smartphone sensing mood machine learning predictive modeling

Persistent Identifier

PsychArchives acquisition timestamp

2022-06-01 13:22:06 UTC

Publisher

PsychArchives

Citation

Download BibTex
Download as Text
  • Author(s) / Creator(s)
    Kunz, Fiona
  • PsychArchives acquisition timestamp
    2022-06-01T13:22:06Z
  • Made available on
    2022-06-01T13:22:06Z
  • Date of first publication
    2022-06-01
  • Abstract / Description
    Momentary experiences of positive and negative emotionality are core components of well-being and performance. This study investigates whether passively sensed smartphone data can be used to recognize individuals’ mood (i.e. Valence and Arousal (Russell, 1980)) based on their smartphone sensing data. The exploratory analysis uses data generated from N = 453 participants in a two-week experience sampling wave which was part of the Smartphone Sensing Panel Study (SSPS; Schödel & Oldemeier, 2020). Different cross-validated machine learning algorithms are compared to predict participants’ current mood given a variety of situational and behavioral variables, reflected by different smartphone sensing modalities. Moreover, the impact of different time perspectives (i.e. daily versus hourly) on the predictive performance is investigated.
    en
  • Publication status
    other
    en
  • Review status
    unknown
    en
  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/6206
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.6895
  • Language of content
    eng
  • Publisher
    PsychArchives
    en
  • Is related to
    https://doi.org/10.23668/psycharchives.2901
  • Keyword(s)
    Smartphone sensing
    en
  • Keyword(s)
    mood
    en
  • Keyword(s)
    machine learning
    en
  • Keyword(s)
    predictive modeling
    en
  • Dewey Decimal Classification number(s)
    150
  • Title
    Smartphones as mood barometers: Predicting mood in daily life using different sensing modalities
    en
  • DRO type
    preregistration
    en
  • Visible tag(s)
    Smartphone Sensing Panel Study
    en