In a study last year, colleagues and I found that people with HIV experienced high levels of fatigue and that their fatigue was predicted by everyday factors like sleep, stress, and mood. These findings showed interesting differences between people’s conscious (Narrative mind) experiences of fatigue and the daily non-conscious factors (Intuitive mind) that actually predicted their day-to-day levels of fatigue.
Knowing this, I wondered whether we could help people to reduce their fatigue by giving them more information about predictors of fatigue that are normally outside of their awareness. I was able to conduct a preliminary test of this idea (pre-COVID) in a single case study where I met with an additional participant who would have qualified for the study, but who enrolled after the main data collection phase was completed and analyses had already begun. This serendipitous timing allowed me to provide the participant with information compiled from all of the other people in the study, and to learn whether this information would help him to better manage his own fatigue.
The participant in this single-case pilot study was a middle-aged Latino man, who had been living with HIV for over 10 years and whose health condition was currently well-managed with antiretroviral treatment. He reported generally good health, except for chronic fatigue that would sometimes interfere with his daily activities. As we had done with other study participants, I gave him a Fitbit wristband to track his daily physical activity, sleep, and heart rate metrics. He also agreed to complete daily surveys on his smartphone about psychological states like mood and stress, once per day at a random time. All of these procedures were identical to those in the main study.
The one procedural difference for this participant was that when I met with him again after 30 days of daily data collection, I not only completed the study's usual outtake paperwork, but also shared the results of the main study with him, and provided a personalized report on fatigue (sample image above with hypothetical data). The report compared the individual participant's results over the last 30 days to those of all other participants who had been in the main study. Here are some features of the report that I thought might be helpful to him:
- The top graph showed his individual average (red line) and range of fatigue scores over the last 30 days (red box). My goal in showing a range of scores was to visually illustrate how his fatigue went up and down over time. The existence of some variability in fatigue scores means that it might be possible to change them through one's actions. Indeed, simply noticing the existence of variations is the start of gaining control over them, as in the example of biofeedback where viewing physiological data in real time allows people to gain control over apparently involuntary bodily processes like blood pressure and heart rate. In Two Minds Theory terms, the Narrative system input allows for changes in Intuitive-level processes on future iterations of the cycle in which Intuitive-level responses lead to behaviors.
- Other lines on the same graph allowed the participant to compare his own fatigue levels to the patterns of fatigue reported by other people in our study. We had been using a short fatigue measure from the PROMIS symptom toolkit, which allowed us not only also to compare his results to those of other people living with HIV, but also to those of people in the general population who don't have a chronic condition like HIV. My goal in offering this feedback was to normalize his experience of fatigue: Sometimes people feel less isolated and find their symptoms more tolerable simply based on learning that they aren’t unusual or unexpected.
- The next several sections of the report summarized variables that were shown in our overall study to predict people's day-by-day fatigue. For example, getting more physical activity led people to feel less tired the next day, a fact that might seem counter-intuitive initially. Not surprisingly, better sleep (in terms of both amount and quality) led to less fatigue for most of our participants. Less stress generally predicted less fatigue, whether measured by a daily questionnaire or physiological metrics like resting heart rate and heart rate variability. Specific stressors like a lack of social support or the presence of HIV-related stigma also predicted fatigue, highlighting the importance of stress that is experienced in the contexts of relationships with others. Better mood and a greater sense of control over life generally predicted less fatigue. And avoidance coping generally predicted more fatigue, a sign that not engaging with problems can make people feel physically tired.
- Within each of those sections, besides the general relationship between each predictor variable and fatigue, I also showed a graph to illustrate the individual participant's scores over 30 days and their relationship with his own self-reported fatigue scores. Sometimes these within-person relationships were stronger than the overall association seen in the main study, and sometimes they were weaker. I quantified each of the relationships with a Pearson correlation (r), which ranges from 0 (no relationship) to 1 (perfect relationship) and includes a + or - sign to show whether the variable tended to increase or reduce fatigue. The range of scores on each variable again suggested that it might be controllable as it went up and down from day to day. And the strength of the relationships -- both overall and for this participant specifically -- suggested areas where he might be able to focus his efforts to reduce fatigue.
- Finally, I provided some general recommendations. These included tailored feedback to summarize the participant's individual scores on the fatigue questionnaire and the strongest predictors of fatigue for him personally. They also included some general advice like "get more physical activity" and "try to get a good night's sleep" that seemed like they would be beneficial even in cases where a variable's within-person relationship to fatigue wasn't that strong.
What difference did all of this information make? I met with the pilot participant after 30 days of sensor and survey data collection (plus a week for me to run the numbers and prepare a report). I gave him his personalized information and we spent an hour or so talking through the data. And then I spoke with him again about a month later to ask how things were going at that time. He reported that his fatigue was still going up and down, but he felt like he could manage it a little better without interference in his everyday activities. (Note that he was no longer completing daily surveys by this time, so I can't provide any numeric data on his later fatigue). I asked what information had been most helpful, and like many participants in our main study he said that he hadn't realized how variable his exercise had been, or how often he failed to get a good night's sleep. He said he was trying to work on both of those things, again with some progress. But mainly, he said, he had been paying more attention to his mood and re-examining some of his reactions to important people in his life. He said that the idea about trying to more actively handle interpersonal stressors rather than passively avoiding them was something that stuck with him, and that he felt his mood was better and his physical fatigue was less when he did so. This was clearly going to be an ongoing effort rather than a simple fix, but overall he felt that he did have more control over his fatigue and that his health was moving in the right direction.
Correlation is of course not causation, and this single-case example has a large number of limitations. There was only one participant and his results might not be replicable. I had only his subjective report to indicate that the feedback made a difference, and that report was somewhat mixed after 1 month of thinking about the results. The feedback was provided all at once, at a single point in time, rather than in real-time as the participant went through his daily life; in a future study it would be interesting to see if more immediate feedback would make a bigger difference. And the participant found some value in variables that weren't specifically predictive of fatigue in his own data, so it might be the case that one could receive feedback about the study's overall results without the extra work of providing highly-personalized recommendations as I did in this pilot activity. But on the other hand, much of the work of preparing a personalized report in the future could be automated with a good database program. It's certainly possible that some feedback topics would be more consistently useful than others, so in future research we could also compare different people's reactions to different components of the feedback, and refine our reports to offer just those that are deemed the most useful, the most modifiable, or the most important as causes of fatigue.
Overall, this single-case example suggests that an individual, highly-tailored feedback report can be generated using a combination of information about general predictors of fatigue and an individual patient's personal experiences connected to fatigue. The report led one participant to some novel insights about things that might be making him feel tired in his own life, and it resulted in him attempting some changes that he felt were at least potentially beneficial in reducing his fatigue. Because fatigue is such a common symptom for people living with chronic illnesses like HIV, and because it's a symptom with no obvious medical cure (there's no pill to treat it), any potentially helpful intervention is probably worth pursuing. A low-cost, low-risk intervention that relies on commonly available sensor and survey technology is particularly interesting, because it could be made available to many people. I hope to get funding for some further research in this area, as an interesting application of Two Minds Theory that could produce benefits for people with HIV or other chronic diseases.
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