You will be shown the abstract of a medical clinical trial report. Your task is to extract all the findings from this report into a JSON array. Each finding should contain the following five elements:

- Intervention: The medical intervention being tested. This should be a text span copied from the input passage.
- Comparator: The baseline against which the intervention is being evaluated. This should be a text span copied from the input passage. If no comparator is reported, set to `null`.
- Outcome: The medical outcome whose effect is being measured. This should be a text span copied from the input passage.
- Effect: The effect of the intervention on the outcome, relative to the comparator. The effect should be one of the following three values: ("significantly increased", "significantly decreased", "no significant difference").
- Evidence: The evidence for the effect. This should be a text span copied from the input passage.

Please format your output as a JSON array. Each entry in the output should be an array containing the 5 elements listed above, in the following order: [<intervention>, <comparator>, <outcome>, <effect>, <evidence>].

For example, an output with two findings might read:
```
[["aspirin", "placebo", "headache severity", "significantly decreased", "Mean headache severity was significantly decreased in the aspirin group compared to the placebo group (p < 0.05)."], ["aspirin", "placebo", "weight loss", "no significant difference", "We did not observe any difference in weight loss between the group given aspirin relative to the control group"]]
```

There are 1 finding(s) in the abstract below. Please extract them. Output only the JSON array with these 1 findings. Do not include any additional text.

Abstract:
ABSTRACT: Television viewers' attention is increasingly more often divided between television and "second screens", for example when viewing television broadcasts and following their related social media discussion on a tablet computer. The attentional costs of such multitasking may vary depending on the ebb and flow of the social media channel, such as its emotional contents. In the present study, we tested the hypothesis that negative social media messages would draw more attention than similar positive messages. Specifically, news broadcasts were presented in isolation and with simultaneous positive or negative Twitter messages on a tablet to 38 participants in a controlled experiment. Recognition memory, gaze tracking, cardiac responses, and self-reports were used as attentional indices. The presence of any tweets on the tablet decreased attention to the news broadcasts. As expected, negative tweets drew longer viewing times and elicited more attention to themselves than positive tweets. Negative tweets did not, however, decrease attention to the news broadcasts. Taken together, the present results demonstrate a negativity bias exists for social media messages in media multitasking; however, this effect does not amplify the overall detrimental effects of media multitasking.

Findings:
[["positive Twitter messages", "negative Twitter messages", "viewing times", "significantly decreased", "As expected, negative tweets drew longer viewing times and elicited more attention to themselves than positive tweets."]]