Need for Cognition Among Users of Self-Monitoring Systems for Physical Activity: Survey Study

Background Need for cognition (NFC) is among the most studied personality traits in psychology. Despite its apparent relevance for engaging with technology and the use of information, it has not been studied in the context of self-monitoring systems and wearables for health. This study is the first to explore the relationship between NFC and commercial self-monitoring systems among healthy users. Objective This study aims to explore the effect of NFC levels on the selection of self-monitoring systems and evaluation of system features of self-monitoring and feedback, as well as perceived credibility and perceived persuasiveness. We also assessed perceived behavior change in the form of self-reported activity after adopting the system. Methods Survey data were collected in October 2019 among university students and personnel. The invitation to respond to the questionnaire was addressed to those who had used a digital system to monitor their physical activity for at least two months. The web-based questionnaire comprised the following 3 parts: details of system use, partially randomly ordered theoretical measurement items, and user demographics. The data were analyzed using structural equation modeling. The effect of NFC was assessed both as 3 groups (low, moderate, and high) and as a continuous moderator variable. Results In all, 238 valid responses to the questionnaire were obtained. Individuals with high NFC reported all tested system features with statistically significantly higher scores. The NFC also had some effect on system selection. Hypothesized relationships with perceived credibility gained support in a different way for individuals with low and high NFC; for those with low NFC, credibility increased the persuasiveness of the system, but this effect was absent among individuals with high NFC. For users with high NFC, credibility was related to feedback and self-monitoring and perhaps continuously evaluated during prolonged use instead of being a static system property. Furthermore, the relationship between perceived persuasiveness and self-reported activity after adopting the system had a large effect size (Cohen f2=0.355) for individuals with high NFC, a small effect size for individuals with moderate NFC (Cohen f2=0.107), and a nonsignificant path (P=.16) for those with low NFC. We also detected a moderating effect of NFC in two paths on perceived persuasiveness but only among women. Our research model explained 59.2%, 63.9%, and 47.3% of the variance in perceived persuasiveness of the system among individuals with low, moderate, and high NFC, respectively. Conclusions The system choices of individuals seem to reflect their intrinsic motivations to engage with rich data, and commercial systems might themselves be a tailoring strategy. Important characteristics of the system, such as perceived credibility, have different roles depending on the NFC levels. Our data demonstrate that NFC as a trait that differentiates information processing has several implications for the selection, design, and tailoring of self-monitoring systems.

Presents statistics of all measured issues with different time-frames, e.g. weekly and monthly views.
Several advanced features depending on the sensors available in the wrist device.
Training features record rich data, including heart rate, pace, distance etc.
Works only with Polar wrist devices.
Activity presented as percentages of the goal, both in app (see figure) and in wrist device.
Activity levels are shown as time spent in each level and how they are located in the day (mobile app).
Inactivity alerts and alerts to notify that the goal of daily activity has been reached (both in app and in wrist device).
Feedback contains also verbal praise.
Brand Name of the mobile application Presents statistics of activities with different time-frames, e.g. weekly and monthly views.
Apple Health is an application that aggregates different types of data, some of them collected via 3 rd party software or iWatch applications.
Basic sensing data is provided by phone sensors and some features of the app function without trackers.
Activity is presented as circles, both in app and in wrist device.
Both the wrist device and mobile application shows current status of each measured parameter.
The user can set goals when iWatch is used. Both app and iWatch give feedback on goal attainment.

Brand
Name of the mobile application

Self-monitoring features Feedback features
Sports Tracker Measures activity when the training/recording session is started in the application.
Measures pace and distance, and uses map views.
Presents statistics of activities with different time-frames, e.g. weekly and monthly views.
Displays goal progress in amount of activity sessions tracked.
Enables setting goals and gives feedback on progress towards goals (see figure).
Brand Name of the mobile application Oura ring works only with mobile app and the ring has no interface or indicators.
Daily activity and inactivity count is presented in the mobile app. Goal is adapted to recovery value (readiness score). Presents its own activity score.
Inactivity alert and activity goal reached feedback is given in mobile app.
Brand Name of the mobile application

Self-monitoring features Feedback features
Garmin Garmin Connect application (iOS, Android) Automatic measuring of activity (e.g. steps, distance, floors, active time, calories) and sleep (several metrics).
Presents statistics of all measured issues with different time-frames, e.g. weekly and monthly views.
Several advanced features depending on the sensors available in the wrist device.
Training features record rich data, including heart rate, pace, distance etc. using maps.
Works only with Garmin wrist devices.
Presents daily activity and several other scores in the main page of the mobile app and in the wrist device.
Enables setting goals and monitoring goal progress (both mobile app and device).
Gives feedback on goal attainment.

Brand
Name of the mobile application Presents statistics of all measured issues with different time-frames, e.g. weekly and monthly views.
Several advanced features depending on the sensors available in the wrist device.
Training features record rich data, including heart rate, pace, distance etc.
Works only with Fitbit wrist devices.
Presents daily activity scores and parameters and several other scores in the main page of the mobile app and in the wrist device.
Enables setting goals and monitoring goal progress (both mobile app and device).
Gives feedback on goal attainment.