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eHealth lifestyle interventions without human support (self-help interventions) are generally less effective, as they suffer from lower adherence levels. To solve this, we investigated whether (1) using a text-based conversational agent (TCA) and applying human cues contribute to a working alliance with the TCA, and whether (2) adding human cues and establishing a positive working alliance increase intervention adherence. Participants (N = 121) followed a TCA-supported app-based physical activity intervention. We manipulated two types of human cues: visual (ie, message appearance) and relational (ie, message content). We employed a 2 (visual cues: yes, no) x 2 (relational cues: yes, no) between-subjects design, resulting in four experimental groups: (1) visual and relational cues, (2) visual cues only, (3) relational cues only, or (4) no human cues. We measured the working alliance with the Working Alliance Inventory Short Revised form and intervention adherence as the number of days participants responded to the TCA's messages. Contrary to expectations, the working alliance was unaffected by using human cues. Working alliance was positively related to adherence (t(78) = 3.606, p = .001). Furthermore, groups who received visual cues showed lower adherence levels compared to those who received relational cues only or no cues (U = 1140.5, z = −3.520, p < .001). We replicated the finding that establishing a working alliance contributes to intervention adherence, independently of the use of human cues in a TCA. However, we were unable to show that adding human cues impacted the working alliance and increased adherence. The results indicate that adding visual cues to a TCA may even negatively affect adherence, possibly because it may create confusion concerning the true nature of the coach, which may prompt unrealistic expectations.

The authors regret that the Standard Deviation (SD) for those who failed their challenge (n = 19,693) was erroneously reported in the Abstract (page 1) and Table 2 of the Results section (page 6) as 3013 steps. The correct Standard Deviation that should have been reported there is 2993 steps. Furthermore, in the Results section under header 3.3 Exploratory Analyses (page 6) we erroneously state that exploratory analyses were performed on a subsample of 29,001 participants. The correct number that should have been reported there is 29,002 participants. The authors would like to apologise for any inconvenience caused., corrigendum voor DOI 10.1016/j.invent.2023.100610, Design Aesthetics, Applied Ergonomics and Design

The authors regret that the Standard Deviation (SD) for those who failed their challenge (n = 19,693) was erroneously reported in the Abstract (page 1) and Table 2 of the Results section (page 6) as 3013 steps. The correct Standard Deviation that should have been reported there is 2993 steps. Furthermore, in the Results section under header 3.3 Exploratory Analyses (page 6) we erroneously state that exploratory analyses were performed on a subsample of 29,001 participants. The correct number that should have been reported there is 29,002 participants. The authors would like to apologise for any inconvenience caused.

Background: Gamification and deposit contracts (a financial incentive in which participants pledge their own money) can enhance effectiveness of mobile behavior change interventions. However, to assess their potential for improving population health, research should investigate implementation of gamified deposit contracts outside the research setting. Therefore, we analyzed data from StepBet, a smartphone application originally developed by WayBetter, Inc. Objective: To perform a naturalistic evaluation of StepBet gamified deposit contracts, for whom they work best, and under which conditions they are most effective to help increase physical activity. Methods: WayBetter provided data of StepBet participants that participated in a stepcount challenge between 2015 and 2020 (N = 72,974). StepBet challenges were offered on the StepBet smartphone application. The modal challenge consisted of a $40 deposit made prior to a 6-week challenge period during which participants needed to reach daily and weekly step goals in order to regain their deposit. Participants who met their goals also received additional earnings which were paid out from the money lost by those who failed their challenge. Challenge step goals were tailored on a 90-day historic step count retrieval that was also used as the baseline comparison for this study. Primary outcomes were increase in step count (continuous) and challenge success (dichotomous). Results: Overall, average daily step counts increased by 31.2 % (2423 steps, SD = 3462) from 7774 steps (SD = 3112) at baseline to 10,197 steps (SD = 4162) during the challenge. The average challenge success rate was 73 %. Those who succeeded in their challenge (n = 53,281) increased their step count by 44.0 % (3465 steps, SD = 3013), while those who failed their challenge (n = 19,693) decreased their step count by −5.3 % (−398 steps, SD = 3013). Challenges started as a New Year's resolution were slightly more successful (77.7 %) than those started during, Corrigendum DOI 10.1016/j.invent.2023.100626, Design Aesthetics, Applied Ergonomics and Design