Becoming More Conscientious or More Open to Experience? Effects of a Two-Week Smartphone-Based Intervention for Personality Change

Abstract

Research indicates that it might be possible to change personality traits through intervention, but this clinical research has primarily focused on changing neuroticism. To date, there are no established, proven techniques for changing other domains of personality, such as conscientiousness and openness. This research examined the effects of a two-week smartphone-based intervention to either change one facet of conscientiousness (i.e. self-discipline) or one facet of openness to experience (i.e. openness to action). Two intervention studies (total N = 255) with two active intervention groups for mutual comparisons were conducted. Results of self-reports and observer reports showed that people who wanted to become more self-disciplined were less self-disciplined at pretest. Similarly, people who wanted to become more open to action were less open to action at pretest. The results showed that people who chose the self-discipline intervention showed greater increases in self-discipline, and people who chose the openness to action intervention showed greater increases in openness to action compared with the other group. Changes were maintained until follow-up two and six weeks after the end of the intervention. Future work is needed to examine whether these personality changes are enduring or reflect temporary accentuation as a result of participation in the intervention.

How Are Personality States Associated with Smartphone Data?

“…Second, smartphone data may be useful for developing, testing, and refining theories that involve personality states, such as whole trait theory (Fleeson & Jayawickreme, 2015). Third, it may be relevant for testing the effectiveness of interventions that are designed to affect personality traits (such as Magidson, Roberts, Collado‐Rodriguez, & Lejuez, 2014; Stieger et al, 2020), because the corresponding state measures should be affected by changes in the trait variable (Horstmann & Ziegler, 2020). Finally, it may allow reducing the burden of survey assessment for study participants by triggering the surveys only when change of behavioural patterns suggest a change in personality state patterns.…”

Descriptive, Predictive and Explanatory Personality Research: Different Goals, Different Approaches, but a Shared Need to Move beyond the Big Few Traits

“…Time‐series data may be combined with experimental designs, such as by experimentally manipulating the X state—for example instructing people to pursue certain goals or to act extraverted—and see if the Y state tends to increase in response (e.g. Margolis & Lyubomirsky, 2019; Stieger et al, 2020). There remain important questions about the extent to which experimentally manipulating psychological states serves as an ecologically valid means of understanding how the states naturally covary, however, due to issues such as self‐selection effects (i.e.…”

“…This allows us to measure a broader selection of narrower traits with the same number of carefully selected items, because fewer conceptually interchangeable items are required for each trait (McCrae & Mõttus, 2019; Wood, Nye, & Saucier, 2010; Yarkoni, 2010). Improved computational power and accessible data analytic tools have eased working with many–dimensional data to efficiently summarize, communicate and compare association patterns (Costantini et al, 2015; Revelle, 2020; Elleman, McDougald, Condon, & Revelle, 2020; Stachl et al, 2020b).…”

“…But often these assessment methods have to be given personality–relevant interpretation in relation to subjectively rated personality traits before they become useful. For example, on its own, mobile phone sensor data do not have psychological meaning; they do once we know how they track with self–reported traits (Hall & Matz, 2020; Stachl et al, 2020b; Wiernik et al, 2020). As a result, these methods often approximate subjectively rated traits rather than provide entirely new information, and any issues with self–reports can spill over to their digital approximations (Tay, Woo, Hickman, & Saef, 2020).…”

“…Truly predictive research aims to create models where characteristics such as personality traits are used to model the best possible predictions of outcomes in data that have not yet been accessed or even collected (out–of–sample prediction). First, this means that the observations that are used to create, or ‘train’, predictive models must not be the same observations that will eventually be predicted (Stachl et al, 2020b; Yarkoni & Westfall, 2017). Second, such research should explore the limits of predictive accuracy, whereas descriptive models often have other priorities, as we argue below.…”