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High-intensity physical activity improves the health of people with HIV. Even when people have good intentions to engage in physical activity, they often find it difficult to maintain physical activity behavior in the long term. Two Minds Theory is a neurocognitive model that explains gaps between people’s intentions and behaviors based on the operations of 2 independent mental systems. This model predicts that everyday experiences will affect physical activity and that factors outside people’s awareness, such as sleep and stress, can have particularly strong effects on physical activity behaviors.
We designed this study to test the effects of daily experiences on physical activity among people with HIV, including measures of people’s conscious experiences using daily electronic surveys and measures of nonconscious influences using sensor devices.
In this study, 55 people with HIV wore a Fitbit Alta for 30 days to monitor their physical activity, sleep, and heart rate variability (HRV) as a physiological indicator of stress. Participants also used their smartphones to complete daily electronic surveys for the same 30 days about fatigue, self-efficacy, mood, stress, coping, motivation, and barriers to self-care. Time-lagged, within-person, multilevel models were used to identify the best prospective predictors of physical activity, considering the daily survey responses of people with HIV and sensor data as predictors of their physical activity the following day. We also tested baseline surveys as predictors of physical activity for comparison with daily variables.
Different people had different average levels of physical activity; however, physical activity also varied substantially from day to day, and daily measures were more predictive than baseline surveys. This suggests a chance to intervene based on day-to-day variations in physical activity. High-intensity physical activity was more likely when people with HIV reported less subjective fatigue on the prior day (
Some predictors of physical activity, such as HRV, were only apparent based on sensor data, whereas others, such as fatigue, could be measured via self-report. Findings about coping were unexpected; however, other findings were in line with the literature. This study extends our prior knowledge on physical activity by demonstrating a prospective effect of everyday experiences on physical activity behavior, which is in line with the predictions of Two Minds Theory. Clinicians can support the physical activity of people with HIV by helping their patients reduce their daily stress, fatigue, and barriers to self-care.
Physical activity is important in managing many chronic diseases, including HIV. With current antiretroviral treatment (ART) options, people with HIV have almost but not quite a normal life expectancy [
Assuming that a patient’s HIV is controlled with ART, prevention of cardiovascular complications in people with HIV involves a number of health promotion strategies similar to those recommended for people without HIV. Hypertension, diabetes, and cholesterol screening are needed, and medications may be required to manage these conditions [
In addition to its cardiovascular benefits, exercise is known to enhance mood and reduce stress, with higher levels of physical activity showing positive effects on many mental health indicators among people with HIV [
Unfortunately, depressive symptoms make it less likely for people with HIV to engage in physical activity; other barriers to physical activity among people with HIV include ART side effects, comorbid health problems, physical pain, lower self-efficacy for exercise, fewer perceived benefits of exercise, and lower motivation for exercise [
The differences between the positive effects of exercise and people’s difficulty in adopting and maintaining exercise routines can be explained by Two Minds Theory (TMT), which is a novel approach to understanding and changing health behaviors based on the idea of
As the intuitive system operates in the moment and in the context of everyday environments, Cook et al [
To better understand the factors that facilitate or interfere with physical activity among people with HIV, we conducted a secondary analysis of data from a mixed method study of daily fatigue experiences among people with HIV [
Recruitment took place in an infectious disease specialty clinic at an academic medical center in Denver, Colorado, United States, from September 2017 to November 2018. The clinic provides care for approximately 1850 people with HIV annually, 97% of whom are on ART, 91% of whom are virally suppressed, and 30% to 45% of whom have significant fatigue [
The inclusion criteria were people with HIV with (1) well-controlled HIV (viral load<20 copies/mL) with current ART, (2) English language fluency, (3) age of 18 to 80 years, and (4) at least mild fatigue based on the Patient-Reported Outcomes Measurement Information System (PROMIS) 4-item screening tool [
Participants wore a Fitbit Alta HR sensor device for 30 days. The overall physical activity was measured based on the total steps per day, whereas high-intensity physical activity was measured based on the number of
Participants also completed the validated EMA survey measures on mood, fatigue, self-efficacy, and other psychological variables by responding to a daily message on their smartphones with a link to a REDCap (Research Electronic Data Capture; Vanderbilt University) web-based data form. The EMA survey included questions about situational variables that might interfere with physical activity, such as travel, substance use, or medication side effects.
After consent was obtained, people with HIV completed baseline questionnaires on fatigue and other symptoms. They provided a release of information to extract demographic and clinical data from their electronic health records and received instructions on how to use the Fitbit device and web-based EMA surveys for the next 30 days. A link to complete the survey was delivered once per day at a random time, either by email or SMS text message (participant’s choice). Participants returned after 1 month to complete additional questionnaires and provide a blood sample, and the first 25 participants completed a qualitative interview about their fatigue experiences and physical activity; these data have been presented elsewhere [
Demographic data from patients’ charts and intake paperwork included age, gender, race, ethnicity, and current employment status. Participants also completed baseline self-report measures, including PROMIS scales for fatigue, depression, applied cognition, sleep, and pain [
The Fitbit wristbands collected continuous ambulatory data on physical activity (total steps and active minutes per day), sleep (time in bed, total sleep time, wake after sleep onset, sleep efficiency, and sleep stages), HR (average, resting, minimum, and maximum), and HRV, with each of the metrics calculated as daily averages for 1 month. Consumer-grade Fitbit monitors provide a balance of sensitivity and data collection efficiency for both activity and sleep data, showing valid step counts compared with research-grade devices and acceptable results for acquiring HRV in field settings [
EMA survey items included the 4-item PROMIS fatigue short form (Cronbach α=.95) [
To quantify the effects of intuitive-level daily variables versus those of narrative-level measures collected once at baseline, we first examined within-person (day by day) and between-person (stable over time) variation on each of the physical activity measures using graphical displays and intraclass correlation coefficients (
We evaluated the effects of stable between-person characteristics using Pearson
We then tested the effects of within-person sensor and survey variables via within-person multilevel models using SPSS (version 27, IBM Corp). Our models used restricted maximum likelihood estimation to generate β coefficients, which compensated for missing data by imputing a distribution within each participant’s scores based on all available data points. The actual number of survey data points was 58.28% (or 943/1618 person-days) of the possible days. Of the 1618 person-days, data completeness for sensor measures varied by type, with 1130 (69.84%) person-days of valid HR and physical activity data but only 890 (55.01%) person-days of sensor-based sleep data.
A time-lagged analysis was used to predict each day’s physical activity from the experiences of people with HIV on the previous day, which allows for causal conclusions based on the assumption that the cause precedes the effect [
All models used fixed effects, a standard diagonal matrix, and group mean centering of predictors to control for the clustering of observations within participants. Under these assumptions, a sample size of 55 people with HIV yielded a power of 0.80 to detect moderate effect sizes (β>.40) in multilevel analyses, assuming up to 5 predictors with moderate multicollinearity (variance inflation factor 2.0), moderate
This study was approved by the Colorado Multiple Institutional Review Board (protocol 16-2603).
Of the 61 people with HIV approached for the study, 55 (90%) agreed to participate. The most common reasons for nonparticipation were (1) too busy for daily surveys, (2) not interested in using sensor devices, or (3) their smartphones being too old or low on memory to add the software needed for the study. The final sample included 85% (47/55) men and 15% (8/55) women. Participants’ age ranged from 20 to 69 years, and 58% (32/55) of the participants were White and non-Hispanic. In addition, the sample included 20% (11/55) of Black participants and 22% (12/55) of Latino/Latina participants. These demographics are typical of people with HIV in Colorado.
Descriptive analyses of the baseline variables showed high levels of fatigue (greater than the PROMIS 50th percentile on 66% of days; 622/943), a high level of perceived stigma related to HIV (78% of days; 718/921), poor mood (at least once for 80% of people with HIV; 42/52), and high stress (at least once for 53% of people with HIV; 28/52). Furthermore, people with HIV frequently had interrupted sleep based on Fitbit data (284/888, 32% of nights), and some (8/40, 20%) had an overall sleep efficiency level <85%, suggesting disordered sleep. Consistent with the idea that even well-managed HIV involves chronic inflammation, participants had a moderate average C-reactive protein level (mean 2.51, SD 3.34 mg/L), and 5% (2/42) had high levels of inflammation based on C-reactive protein >10. Participant demographics were described in greater detail by Makic et al [
Physical activity varied dramatically both between and within individuals.
Steps per day for each study participant. Black dots represent each person’s average steps across all days that they wore the Fitbit sensor device (up to 1 month for each participant). Gray bars represent the within-person average (SD 1). SDs were calculated within individuals, and therefore, the varying height of the bars reflects the fact that some people’s daily amount of physical activity varied more than that of others. The dashed line at 10,000 steps reflects the recommended daily amount of overall physical activity for adults.
Active minutes per day for each study participant. Black dots represent each person’s average number of active minutes per day, across all days that they wore the Fitbit sensor device (up to 1 month for each participant). Gray bars represent the within-person average (SD 1). SDs were calculated within persons, and therefore, the varying height of the bars reflects the fact that some people’s daily amount of physical activity varied more than that of others. The dashed line at 30 minutes per day reflects the recommended daily amount of active minutes for adults.
In the multivariable model, the maximum HR and all of the sleep variables were no longer significant. As shown in the right-hand column of
A smaller set of daily variables predicted high-intensity physical activity (
Maximum HR, self-efficacy, and stress were excluded from the multivariable model. Therefore, lower fatigue, less use of approach coping, less HIV-related stigma, and fewer perceived barriers were the only significant EMA predictors of next day active minutes, along with the sensor measures of average HR and lower HRV.
Predictors of next day total steps.
Daily predictor variable | Univariate model | Combined model | ||||||
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β (SE) | β (SE) | ||||||
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Resting HR (bpmb) | 4.74 (16.0) | 0.09 (83) | .77 | —c | — | — | |
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Maximum HR (bpm) | 33.8 (8.67) | 15.2 (520) | <.001 | — | — | — | |
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Average HR (bpm) | 133 (15.2) | 76.3 (733) | <.001 | 325 (20.3) | 256 (149) | .006 | |
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Minimum HR (bpm) | –45.2 (22.9) | 3.88 (520) | .05 | –215 (34.9) | 38.2 (231) | .01 | |
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HRVd (mspbe) | 14.5 (4.33) | 11.2 (52) | .008 | 22.8 (5.00) | 20.8 (79) | .03 | |
|
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Total sleep time (minutes) | –1.43 (1.32) | 1.18 (473) | .28 | — | — | — | |
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Time in bed (minutes) | –1.19 (1.16) | 1.05 (467) | .31 | — | — | — | |
|
Sleep efficiency (%) | –7663 (3658) | 4.39 (376) | .04 | — | — | — | |
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Interrupted sleep (yes or no) | –719 (377) | 3.65 (335) | .06 | — | — | — | |
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Wake after sleep onset (number) | 96.9 (47.0) | 4.25 (431) | .04 | — | — | — | |
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Light sleep (%) | –449 (1719) | 0.07 (98) | .80 | — | — | — | |
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Deep sleep (%) | 10,546 (2970) | 12.6 (274) | .001 | — | — | — | |
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REMf sleep (%) | –6978 (1423) | 24.0 (406) | .03 | — | — | — | |
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PROMISg fatigue ( |
–95.9 (9.80) | 95.8 (338) | <.001 | –46.6 (17.3) | 7.25 (260) | .008 | |
|
Self-efficacy (1-4 scale) | 734 (435) | 2.84 (41) | .09 | — | — | — | |
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Mood (1-4 scale) | 66.2 (310) | 0.05 (161) | .83 | — | — | — | |
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Stress (1-4 scale) | –414 (348) | 1.41 (300) | .24 | — | — | — | |
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Avoidance coping (1-4 scale) | 1114 (287) | 15.1 (295) | .001 | 964 (244) | 15.7 (245) | <.001 | |
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Approach coping (1-4 scale) | 589 (336) | 3.08 (429) | .08 | — | — | — | |
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Social support (1-4 scale) | 822 (278) | 871 (445) | .003 | 530 (254) | 4.37 (237) | .04 | |
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Stigma (1-4 scale) | –29.1 (288) | 0.01 (355) | .92 | — | — | — | |
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ARTh adherence (yes or no) | –526 (1385) | 0.14 (134) | .71 | — | — | — | |
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Motivation (1-7 scale) | 97.3 (623) | 0.02 (367) | .88 | — | — | — | |
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Barriers (1-4 scale) | –3590 (597) | 36.1 (221) | <.001 | –1396 (514) | 7.37 (221) | .008 |
aHR: heart rate.
bbpm: beats per minute.
cOnly statistically significant results are reported.
dHRV: heart rate variability.
emspb: milliseconds per beat.
fREM: rapid eye movement.
gPROMIS: Patient-Reported Outcomes Measurement Information System.
hART: antiretroviral treatment.
Predictors of next day active minutes.
Daily predictor variable | Univariate model | Combined model | |||||
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β (SE) | β (SE) | |||||
|
|||||||
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Resting HR (bpmb) | .26 (.26) | 1.03 (528) | .31 | —c | — | — |
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Maximum HR (bpm) | .91 (.10) | 82.9 (99) | <.001 | — | — | — |
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Average HR (bpm) | 1.77 (.19) | 90.9 (768) | <.001 | 3.21 (.26) | 155 (284) | <.001 |
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Minimum HR (bpm) | .35 (.26) | 1.77 (512) | .18 | — | — | — |
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HRVd (mspbe) | .41 (.05) | 55.1 (616) | <.001 | .66 (.07) | 79.6 (259) | <.001 |
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Total sleep time (minutes) | –.02 (.02) | 0.97 (488) | .33 | — | — | — |
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Time in bed (minutes) | –.01 (.01) | 0.67 (507) | .41 | — | — | — |
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Sleep efficiency (%) | –41.9 (44.4) | 0.89 (536) | .35 | — | — | — |
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Interrupted sleep (yes or no) | –4.96 (4.67) | 1.13 (517) | .29 | — | — | — |
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Wake after sleep onset (number) | –.20 (.56) | 0.13 (205) | .72 | — | — | — |
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Light sleep (%) | –.01 (.02) | 0.09 (98) | .76 | — | — | — |
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Deep sleep (%) | –.03 (.04) | 0.54 (274) | .46 | — | — | — |
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REMf sleep (%) | –.04 (.06) | 0.38 (406) | .54 | — | — | — |
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PROMISg fatigue ( |
–1.81 (.27) | 45.4 (185) | <.001 | –.98 (0.25) | 15.4 (287) | <.001 |
|
Self-efficacy (1-4 scale) | 12.2 (5.30) | 5.31 (72) | .02 | — | — | — |
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Mood (1-4 scale) | 7.43 (3.85) | 3.72 (108) | .06 | — | — | — |
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Stress (1-4 scale) | –9.45 (4.23) | 4.99 (51) | .03 | — | — | — |
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Avoidance coping (1-4 scale) | 1.04 (3.69) | 0.08 (165) | .78 | — | — | — |
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Approach coping (1-4 scale) | –10.4 (4.22) | 6.05 (198) | .02 | –8.50 (3.77) | 5.08 (390) | .03 |
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Social support (1-4 scale) | 6.07 (3.46) | 3.09 (226) | .08 | — | — | — |
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Stigma (1-4 scale) | –9.33 (3.47) | 7.24 (50) | .01 | –9.59 (3.40) | 7.98 (355) | .005 |
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ARTh adherence (yes or no) | –2.27 (19.8) | 0.01 (180) | .91 | — | — | — |
|
Motivation (1-7 scale) | .79 (8.39) | 0.01 (307) | .93 | — | — | — |
|
Barriers (1-4 scale) | –35.5 (8.03) | 19.6 (352) | <.001 | –22.4 (7.33) | 9.34 (386) | .002 |
aHR: heart rate.
bbpm: beats per minute.
cOnly statistically significant results are reported.
dHRV: heart rate variability.
emspb: milliseconds per beat.
fREM: rapid eye movement.
gPROMIS: Patient-Reported Outcomes Measurement Information System.
hART: antiretroviral treatment.
Effects of demographic and narrative-level predictors.
Baseline variable | Effect on total steps | Effect on active minutes | ||||||
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Employed versus not | –0.19 | –0.92 (22) | .36 | –0.19 | -0.93 (22) | .09 | |
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Gender (male or female) | 0.003 | 0.01 (10) | .99 | 0.28 | 0.94 (10) | .37 | |
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Race (any minority status) | 0.05 | 0.28 (26) | .78 | 0.04 | 0.19 (26) | .85 | |
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Age (years) | 0.03 | 0.23 (54) | .82 | 0.18 | 1.42 (54) | .16 | |
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Fatigue (PROMISa tool) | 0.02 | 0.15 (54) | .88 | –0.15 | -1.14 (54) | .26 | |
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Mood (PROMIS tool) | –0.28 | –1.92 (54) | .06 | –0.28 | -2.22 (54) | .03b | |
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Confusion (PROMIS tool) | 0.24 | 1.92 (54) | .06 | 0.40 | 3.23 (54) | .002c | |
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Sleep (PROMIS tool) | 0.05 | 0.39 (54) | .70 | 0.05 | 0.31 (54) | .76 | |
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Pain (PROMIS tool) | –0.08 | –0.61 (54) | .54 | –0.03 | -0.23 (54) | .82 | |
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Stress (HIV-QoLd subscale) | –0.25 | –1.97 (54) | .06 | –0.11 | -0.81 (54) | .42 | |
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Stigma (HIV Stigma scale) | –0.12 | –0.92 (54) | .36 | –0.32 | -2.66 (54) | .01b |
aPROMIS: Patient-Reported Outcomes Measurement Information System.
bSignificant at
cSignificant at
dHIV-QoL: HIV-related Quality of Life scale.
None of the demographic variables predicted either of the physical activity variables, and none of the baseline questionnaire measures had any effect on the total steps measure of physical activity. Three survey measures predicted active minutes: mood, cognitive confusion, and stigma. Some variables that were significant as day-by-day measures at the intuitive level of analysis failed to predict physical activity when measured at the narrative level, including stress and fatigue. Mood and cognitive confusion had the opposite pattern, with small to moderate effects on active minutes when measured at the narrative level only. Stigma predicted active minutes when measured at either the narrative or intuitive level, although it had a stronger relationship with active minutes when measured day by day.
The daily physical activity of people with HIV was predicted based on their previous day environments, experiences, and behaviors. Overall, this study found substantial within-person variability in physical activity and illustrated the importance of everyday experiences and behaviors in understanding daily levels of activity among people with HIV, as suggested by TMT. To our knowledge, this is the first study to examine EMA daily survey measures as predictors of physical activity in people with HIV. Survey-based predictors of overall physical activity in people with HIV included greater avoidance coping, higher perceived social support, and fewer reported barriers to self-care, including the absence of alcohol use, drug use, ART side effects, feeling unwell, and irritation about medication use. The finding on coping was in an unexpected direction; however, other findings were consistent with barriers to physical activity that have been reported in previous studies of people with HIV [
This study also examined sensor-based predictors of physical activity, which provided data on everyday experiences that might have affected the behavior of people with HIV outside of their conscious awareness. Significant predictors of next day total steps included maximum and average HR, both of which might reflect cardiovascular fitness, as well as HRV, which is a physiological indicator of stress. In contrast to this physiologically based stress metric, participants’ self-reported stress was a less useful predictor that had no association with next day total steps, although it weakly predicted next day active minutes in a univariate model. On the other hand, next day physical activity was more consistently related to subjective fatigue based on the PROMIS tool than to sensor-based measures of sleep quality.
Findings about high-intensity physical activity were generally consistent with those for overall activity, although there were fewer significant predictors for the high-intensity level of physical activity that is more important in preventing cardiovascular disease. High fatigue, high levels of HIV-related stigma, and high stress based on HRV are risk factors for inactivity that can be identified by monitoring everyday psychological states. All of these are potentially modifiable risk factors, which could be addressed by clinicians either directly or with a referral to mental health support services. In a prior study, poor mood was a unique prospective marker for days when people with HIV were less likely to miss a dose of ART medication [
In this study, variables from EMA surveys and wrist-worn sensors were found to prospectively predict participants’ physical activity on the day after the variable was measured. The daily experiences measured in this study are considered to represent the intuitive system identified by TMT [
For a direct comparison between narrative- and intuitive-level predictors, we also examined the effects of stable participant demographic characteristics and the effects of predictor variables assessed using standard retrospective questionnaires at a single point in time. None of these variables were significantly associated with steps per day, and only three of the questionnaire measures—depressed mood, cognitive confusion, and HIV stigma—significantly predicted active minutes. The best narrative-level measures had small to moderate effects, despite the fact that we used best practice PROMIS tools to measure constructs of interest. In general, our findings suggest that intuitive-level measures are more useful than narrative-level measures for predicting the physical activity of people with HIV, although one of the predictors (mood) was significant only when it was measured at the narrative level.
Participants in this study were people with HIV recruited from a Ryan White clinic in Denver, Colorado, United States. Of the 1.2 million people currently living with HIV in the United States, approximately 59% are currently in care [
This study was also limited by a moderate sample size, which may reduce generalizability, although the collection of data on multiple days from each participant increased the effective sample size for multilevel analyses. We used a minimally restrictive α level of .05 to identify potential predictors of physical activity in this initial study of everyday experiences; however, there is a risk of type 1 error in our findings. Future studies with larger sample sizes might use more restrictive statistical assumptions to further narrow the number of predictors of physical activity. In addition, Fitbits are not generally viewed as research-grade devices, although they have shown an ability to differentiate sedentary individuals from moderately active individuals in prior studies. Future research could further clarify the relative importance of predictor variables on physical activity by using research-grade activity sensors that have less measurement error, thereby providing greater statistical power for analyses.
Finally, there may be some measurement challenges in this study: the interpretation of HR metrics, including HRV, is not always clear and may be affected by overall cardiovascular fitness and participants’ psychological states. Other daily measures were self-report surveys, and although the tools were validated in prior EMA studies, there is some potential for bias based on self-presentation or inaccurate recall. Findings on coping were in an unexpected direction, which might also be an indication of measurement problems specific to this construct; for example, a participant’s use of more coping strategies might suggest ineffective or inefficient coping, whereas a high score on just 1 of the 9 items might suggest successful coping. Alternatively, it might be that some participants specifically used exercise as a form of coping; although exercise is a healthy behavior, it could be considered avoidance coping as it does not directly engage with the source of stress. If this is the explanation, it might even be the case that exercising close in time to the source of stress results in more effective coping later on (eg, at a time point that is 2 steps removed from the initial stressor). Thus, the unexpected finding about coping presents intriguing possibilities that could benefit from future research.
Our findings illustrate the need for improved physical activity among people with HIV based on day-to-day variability, even among people with HIV who had average daily activity levels above those recommended by health promotion guidelines. Within-person variability was particularly apparent with regard to high-intensity physical activity. Given that everyday experiences such as stress, fatigue, and HIV-related stigma were found to interfere with high-intensity physical activity, clinicians should ask people with HIV about their stress and fatigue. Clinicians can also help their patients develop strategies to address barriers such as stigma and provide referrals to mental health resources, as appropriate. Finally, clinical care environments should take steps to reduce HIV-related stigma and make people with HIV feel welcomed. This may help to reduce the overall barriers to health care for people with HIV, as well as support their physical activity.
Further research is needed on interventions to increase physical activity among people with HIV. In-the-moment interventions might target improvement in EMA-measured variables such as HIV-related stigma and fatigue, as well as sensor-based predictors such as stress based on HRV. TMT suggests that different variables might be important in initiating versus maintaining physical activity over time; however, this study did not differentiate new versus established exercise habits, which might be another important question to investigate in future intervention studies. Finally, research on physical activity using EMA is still relatively novel, and questions related to the accuracy of measurements using both sensors and surveys are also important topics for ongoing investigation. Future studies using both EMA and sensor data can enhance our understanding of the physical activity and other everyday behaviors of people with HIV.
Overall, this study suggests an opportunity to improve how people with HIV manage everyday challenges in order to enhance their physical activity. People with HIV reported significant levels of fatigue, which predicted both total steps and high-intensity physical activity. Subjective fatigue was a better predictor of high-intensity physical activity than actual sleep stages, as estimated using the Fitbit device. In addition, stress predicted physical activity; however, a physiological stress measure, HRV, was a stronger predictor of both total steps and active minutes than self-reported daily stress. Other daily psychological experiences, including self-efficacy, coping, and HIV-related stigma, also predicted physical activity, as did everyday self-care barriers such as alcohol use and ART side effects. Many of these are potentially modifiable variables that could be targeted by clinicians or in future research interventions to improve the physical activity of people with HIV.
antiretroviral treatment
Diary of Ambulatory Behavioral States
ecological momentary assessment
heart rate
heart rate variability
intraclass correlation coefficient
Patient-Reported Outcomes Measurement Information System
Research Electronic Data Capture
Two Minds Theory
This study received funding from the University of Colorado Nursing Biobehavioral Area of Excellence and support from the Colorado Clinical and Translational Science Institute, National Institutes of Health UL1 TR002535.
None declared.