Sustained input of lifelog data is critical for conversational health applications, where algorithms and AI advise users based on recorded lifelog data such as meals, exercise, and sleep. However, an effective method for presenting information to encourage this continuity has not been identified. This study developed three intervention methods for prompting lifelog entries: (a) wording adjustment based on individual characteristics, (b) timing adjustment based on physical activity, and (c) a combination of these adjustments. An empirical experiment with 422 participants was conducted to evaluate the effects.