Usability of a Two-Way Personalized Mobile Trainer System in A Community-Based Exercise Program for Adults with Chronic Traumatic Brain Injury

Abstract

Objective:

To examine the usability of an Apple Watch-based, two-way Personalized Mobile Trainer (PMT) in community-based exercise programs for individuals with chronic TBI (cTBI).

Methods:

This is a prospective pilot study. Twenty participants with cTBI aged 46–73 were enrolled in a 3-month individualized exercise program. After one in-person training session on PMT and exercise program, participants were prescribed either aerobic exercise training (AET) or stretching and toning (SAT) performed at home. The PMT was used to remotely deliver updated exercise prescription, track exercise progress, and communicate with the participants. The primary outcome was compliance with the exercise programs.

Results:

All the participants completed the assigned exercise program with an average compliance of 76%. Nineteen (95%) participants were able to use the PMT properly during exercise sessions. After 3 months of training, the AET trended towards maintaining exercise endurance when compared with the SAT group (0.3% vs −4%, p= 0.14) with a medium effect size of 0.43.

Conclusion:

Using the PMT system to support and track exercise in community-based exercise programs is feasible. The PMT may promote compliance with the training program but testing its effectiveness with larger trials is warranted.

INTRODUCTION:

Regular physical activity improves cardiorespiratory fitness and lowers risk of all-cause mortality and cardiovascular disease.¹ Physical activity may also enhance cognitive function in middle-aged and older adults by improving brain perfusion.^{2, 3} Therefore, the Centers for Disease Control and Prevention (CDC) recommends that all adults who are able, including individuals with chronic disease and disabilities, should engage in at least 150 minutes of moderate-intensity aerobic activity or 75 minutes of vigorous-intensity aerobic activity plus muscle-strengthening activities 2 or more days every week.¹ Although the proportion of adults meeting physical activity guidelines has increased since the release of these CDC guidelines in 2008, only 24% of Americans met the combined aerobic and muscle-strengthening physical activity guidelines in 2017. Despite the benefits of physical activity for middle- and older-age adults, even fewer older adults meet these guidelines (21% in 45–64 year-olds and 14% in ≥ 65 year-olds).⁴ About a quarter of all adults with disabilities are physically inactive.⁵

Promoting physical activity is challenging. Approximately 50% of individuals who start an aerobic exercise program stop within the first 6 months.⁶ Individuals with chronic traumatic brain injury (cTBI) often experience fatigue, pain, physical disabilities, and impaired executive function after injury. Middle- and older-age individuals with cTBI have a higher risk of experiencing cognition decline and dementia.⁷ As a result, this population may have higher risk for nonadherence to an exercise routine than their non-injured peers.^8–10 In our recent pilot study of a community-based exercise program among individuals with cTBI, 80% of 20 participants completed the full 3-month program, but half of those completers finished fewer than 70% of the assigned exercise sessions or did not provide a complete training log.¹¹ As the first unsupervised exercise program conducted in individuals with cTBI, our pilot study results suggest that strategies to engage and support individuals with cTBI to improve compliance with recommended physical activity guidelines are critical for promoting exercise in this population.

Factors that affect adherence to exercise programs in older adults include socioeconomic status, expectations and knowledge about the exercise program, flexibility and design of the exercise program, supervision, multidisciplinary team structure, and the use of technology.¹² Close supervision with an actual health coach has been shown to help with enchancing compliance and motivation. However, this strategy is costly and unavailable to most, reducing any generalizability from research dependent on one-to-one coaching.

Most Americans (85%) now own a smartphone, and almost 50% of U.S mobile phone subscribers are using an Apple device.¹³ Mobile health (mHealth) technology has blossomed as a potentially cost-effective way to improve access to care, self-management, and long-term outcomes in clinical care and TBI research.¹⁴ We recently developed a Personalized Mobile Trainer (PMT) mHealth System using Apple Watch and iPhone to monitor and promote exercise during a community-based exercise program. Different from other workout applications, the PMT is a two-way system which allows the clinical team/trainer to remotely deliver and adapt exercise prescriptions, monitor exercise progress, and communicate with the participants.

The overall goal of this pilot study was to determine the use of the PMT in community-based exercise programs for older persons with cTBI. We determined that feasibility of use of the PMT would exist if individuals with cTBI were able to: 1) learn to use the PMT; 2) demonstrate compliance (>75% of prescribed minutes of exercise completed at target intensity level); and 3) report above average satisfaction with the PMT [System Usability Scale (SUS) score > 68 and rated as good or excellent by > 80% of participants on Client Satisfaction Questionnaire-8 (CSQ-8)].

METHODS:

Participants

Individuals with moderate to severe TBI were recruited from the North Texas TBI Model System and from participants who completed the Group Lifestyle Balance for TBI (GLB-TBI)¹⁵ study, through advertisement at University of Texas Southwestern (UTSW) Medical Center TBI clinics, and from community-based TBI support groups in North Texas.

Inclusion criteria were: 1) history of moderate to severe TBI defined as posttraumatic amnesia (PTA) >24 hours, loss of consciousness (LOC) >30 minutes, Glasgow coma scale (GCS) <13, or intracranial neuroimaging abnormalities within 72 hours prior to hospital admission;¹⁶ 2) single head injury with non-penetrating mechanism; 3) >1 year after initial injury; 5) 45 to 80 years old at the time of consent; 6) walking as the primary means of locomotion [Functional Independence Measure (FIM)¹⁷ locomotion score >5]; and 7) reported persistent symptoms on the Rivermead Post-Concussion Symptoms Questionnaire (RPQ).¹⁸

Exclusion criteria were: 1) participation in regular aerobic exercise defined as a Rapid Assessment of Physical Activity (RAPA) questionnaire score ≥6 (30 minutes or more a day of moderate physical activitys for 5 or more days a week or 20 minutes or more a day of vigorous physical activity for 3 or more days a week) ¹⁹ within the last 2 years; 2) any medical conditions that might prevent participation in exercise, including but not limited to uncontrolled hypertension (systolic BP >220 mmHg or diastolic BP >120 mg Hg), significant cardiac arrhythmia, major psychiatric or neurological disorders (e.g., Parkinson’s disease, dementia); and 3) insufficient English fluency to complete neuropsychological tests.

An initial screening interview was conducted via telephone. Eligible persons who reported persistent neurological symptoms on RPQ and sedentary lifestyle (RAPA <6) in the last 2 years were subsequently invited for an in-person screening visit which included a physical examination, electrocardiogram, and a review of medical history and medications.

Written consent was obtained from all participants prior to study participation, and all study procedures were approved by the UTSW Institutional Review Board.

Study Design

This was a pilot study to determine the efficacy of using the PMT system in community-based exercise programs for older adults with cTBI. In order to determine if the PMT system could track different levels of exercise intensity, the participants were prospectively assigned to either a 3-month aerobic exercise training (AET) group or a 3-month stretching and toning (SAT) group which were age and sex-balanced between groups. Both groups used the PMT system, received the same level of attention from the research staff, and exercised at the same duration and frequency. Eight participants received an iPhone 6 with prepaid data plan and 14 received an Apple Watch 3. The remaining participants already owned these devices.

Patient Health Questionnaire-9 (PHQ9), and Pittsburgh Sleep Quality Index (PSQI) questionnaires were performed to evaluate depression symptoms and sleep quality because these comorbilities might potentially affect the engagment during the program.

Intervention

Exercise Prescription

Exercise prescriptions consisted of exercise intensity, duration and frequency, and mode. A detailed exercise training protocol has been described in our previous publication.¹¹

In both groups, exercise duration was gradually increased from 60 total minutes in Week 1 to 150 total minutes a week (30 mins x 5 days) by the end of Week 4. They maintained at that level for the remaining 8 weeks.

A 6-minute walk test (6MWT) was conducted during the initial visit according to a standardized protocol to determine baseline exercise endurance and exercise intensity.^{20, 21} Exercise intensity for each participant in the AET group was determined based on their: 1) estimated maximal HR (HRmax = 220-age) and 2) maximal heart rate (HR) and reported Borg Rating of Perceived Exertion (RPE) scale during 6MWT. In the SAT group, exercise intensity was determined by HR reserve, calculated using the following formula: (estimated HRmax − HR at rest)*50% + HR at rest. For participants who were on beta-blockers, the Borg RPE scale was used to set exercise intensity (RPE ≥ 17 for AET group and < 11 for SAT group).²²

The participants in both groups were allowed to choose their preferred exercise mode as long as they could meet the expected duration, frequency and intensity target. The most commonly used exercise modes in AET group were fast walking/jogging in the neighborhood, bicycling, and treadmill. Participants in SAT group were provided a stretching handout and encouraged to use other publically available workout apps (e.g. Fiton) to guide stretching or yoga positions targeting both the upper and lower body.

The role of PMT in exercise program

The PMT system comprised a workout app (TBICare), chat app (TBICare Chat), and a Cloud-based Provider Portal (Figure 1).²³ TBICare and TBICare Chat were installed on the iPhone. The PMT Provider Portal is a HIPAA-compliant system hosted on Amazon Web Service (AWS) with user-restricted login access.

Figure 1. Personal Mobile Trainer (PMT) system.

Personalized Mobile Trainer (PMT) system consists of three components: 1) TBICare App installed on the participant’s iPhone and Apple Watch and 2) Provider Portal on HIPAA-Compliant Amazon Web Service (AWS), and 3) TBICare Chat which is a HIPAA-protected chat app on the participant’s iPhone. A. The personalized exercise prescription is entered through Provider Portal and delivered to TBICare App on the iPhone. B. The participant initiates the workout session on the Apple Watch. C. After each workout session, the participant is asked to complete Borg Rating of Perceived Exertion scale and an emotional response survey in the TBICare app on the iPhone. D. The physiological data during the workout session and the post-workout surveys are uploaded to the cloud. E. The care team reviews the patient’s exercise data weekly through Provider Portal. F. Feedback is provided to the participant through TBICare Chat and notification function. The participant can also communicate with the care team via TBICare Chat.

The customized exercise prescription was delivered to the TBICare app on the participant’s iPhone via the PMT Provider portal every week (Figure 1A). During the study period, participants were asked to use TBICare during all the exercise sessions. TBICare extracted workout information including date/time, HR, and exercise duration from the Apple Watch Apple Health kit during each workout session (Figure 1B). At the completion of each exercise session, the TBICare app prompted participants to complete the Borg RPE scale to report their level of exertion during the exercise session (Figure 1C). In addition, participants provided ratings of each session as “Too hard”, “Just Right”, or “Too Easy” with visual cues. After that, the workout data and survey responses were uploaded to the PMT Provider Portal immediately (Figure 1C).

Research staff reviewed exercise progress (HR, duration, frequency, and Borg RPE scale) in the PMT Provider Portal weekly (Figure 1D) and updated the prescription as scheduled if the participant met the exercise goal. If participants’ adherence to exercise was below the prescribed duration or frequency, a reminder message was sent via the PMT system (Figure 1E) and additional telephone meetings were held to assist the participant in solving barriers to exercise and to encourage participants to continue the program.

Participants training

All participants received one initial session of in-person training with a physical therapist. During the initial training, research staff also helped participants develop an exercise schedule, select workout modes, and install the TBICare and TBICare Chat on their iPhone/Apple watch. Participants were trained during the initial session to use these apps and to score the Borg RPE scale after each workout, so they could confidently use the PMT at home. A written user manual and a video of how to use TBICare and TBIChat were also provided to all participants. Participants had one week to familiarize themselves with the apps before they officially started the 3-month exercise program. Additional in-person training for using TBICare was also offered if participants had difficulty using the app.

Outcome Measures

Exericse Endurance

A 6MWT was performed using an internal hallway with the 100-foot distance marked by colored tape on the wall^{20, 21, 24} during the initial visit and post-interventional visit to determine the interval change of exercise endurance. Participants wore an Apple Watch with a pre-loaded 6-minute walk prescription in the TBICare app. They were instructed to pace themselves so they could walk as far as possible without interruption for six minutes.

Compliance

Workout data including HR and duration of each exercise session were downloaded from the PMT at the end of the study. A completed exercise session was defined as a workout session during which the participant met their exercise intensity target for a duration of ≥10 minutes. Weekly compliance was calculated as the sum of all minutes of exercise during the week from completed exercise sessions divided by the total minutes prescribed that week. Overall compliance was calculated as the average across all 12 weeks of the training programs.

User Satisfaction measures

User Satisfaction was measured with CSQ-8, SUS, and a semi-structured interview. The CSQ-8 comprises 8 questions rated on a 1 (lowest satisfaction) to 4 (highest satisfaction) point ordinal scale (Table 2). It measures general satisfaction with the program and has demonstrated high internal consistency.²⁵ The SUS is a 10 question Likert-type scale to assess the usability and learnability of cell phone technologies and mobile applications.²⁶ Each question of the SUS is rated from 1 (strongly disagree) to 5 (strongly agree) (Table 2). Responses are used to generate an overall score range from 1–100 based on a formula provided by the developer (100 is an excellent user experience, 68 is considered average, and anything below 68 is below average).²⁶ Both the CSQ-8 and SUS were sent to participants electronically via REDcap at the end of the study.²⁷ Lastly, research staff conducted a semi-structured interview to collect qualitative feedback from participants regarding their overall experience with the exercise programs and the PMT.

Table 2.

Participants Satisfaction

	Total	AET Group	SAT Group	p
Overall Exercise Program Satisfaction - Client Satisfaction Questionnaire-8 (CSQ-8)
1_How would you rate the quality of service you received? (1–4)	3.35 ± 0.67 (2–4)	3.2 ± 0.63 (2–4)	3.5 ± 0.71 (2–4)	0.32
2_Did you get the kind of service you wanted? (1–4)	3.2 ± 0.95 (1–4)	3.2 ± 0.77 (2–4)	3.2 ± 1.13 (1–4)	0.80
3_To what extent has our program met your goals? (1–4)	3.1 ± 0.72 (2–4)	3.1 ± 0.88 (2–4)	3.1 ± 0.57 (2–4)	0.97
4_If a friend were in need of similar help, would you recommend our program to him or her? (1–4)	3.2 ± 0.77 (2–4)	3.0 ± 0.67 (2–4)	3.4 ± 0.84 (2–4)	0.25
5_How satisfied are you with the amount of help you received? (1–4)	3.4 ± 0.75 (2–4)	3.2 ± 0.79 (2–4)	3.5 ± 0.71 (2–4)	0.44
6_Have the services you received helped you to deal more effectively with your problems? (1–4)	3.0 ± 0.73 (2–4)	2.9 ± 0.74 (2–4)	3.1 ± 0.74 (2–4)	0.58
7_In an overall, general sense, how satisfied are you with the service you have received? (1–4)	3.4 ± 0.81 (2–4)	3.2 ± 0.79 (2–4)	3.5 ± 0.85 (2–4)	0.39
8_If you were to seek help again, would you come back to our program? (1–4)	26.2 ± 4.4 (17–32)	3.3 ± 0.48 (3–4)	3.1 ± 0.88 (2–4)	0.68
CSQ_Total (1–32)	26.1 ± 4.39 (17–32)	24.9 ± 4.5 (17–32)	27.4 ± 4.09 (20–31)	0.28
TBICare App Satisfaction - System Usability Scale (SUS)
1_ I think that I would like to use TBIcare App frequently (1–5)	3.7 ± 1.18 (2–5)	3.4 ± 1.33 (2–5)	4.0 ± 1.00 (3–5)	0.34
2_I found TBIcare App unnecessarily complex (1–5)	2.5 ± 1.30 (1–5)	2.1 ± 1.05 (1–4)	2.9 ± 1.45 (1–5)	0.30
3_I thought TBIcare App was easy to use (1–5)	3.9 ± 1.28 (1–5)	3.9 ± 1.05 (2–5)	3.9 ± 1.53 (1–5)	0.73
4_I think that I would need the support of a technical person to be able to use TBIcare App (1–5)	1.8 ± 1.00 (1–5)	1.9 ± 0.67 (1–3)	1.8 ± 1.30 (1–5)	0.55
5_I found the various functions in TBIcare App were well integrated (1–5)	3.8 ± 1.15 (2–5)	3.9 ± 1.05 (2–5)	3.8 ± 1.30 (2–5)	0.93
6_I thought there was too much inconsistency in TBIcare App (1–5)	2.6 ± 1.29 (1–5)	2.3 ± 1.22 (1–4)	2.8 ± 1.39 (1–5)	0.55
7_I would imagine that most people would learn to use TBIcare App very quickly (1–5)	3.9 ± 1.13 (1–5)	3.8 ± 0.97 (2–5)	4.0 ± 1.32 (1–5)	0.49
8_I found TBIcare App very cumbersome (awkward) to use (1–5)	2.3 ± 1.50 (1–5)	2.2 ± 1.30 (1–4)	2.4 ± 1.74 (1–5)	0.86
9_I felt very confident using TBIcare App (1–5)	3.9 ± 1.45 (1–5)	4.1 ± 1.27 (2–5)	3.7 ± 1.66 (1–5)	0.61
10_I needed to learn a lot of things before I could get going with TBIcare App (1–5)	2.6 ± 1.53 (1–5)	2.6 ± 1.51 (1–5)	2.7 ± 1.66 (1–5)	0.93
SUS _Total (1–100)	69 ± 25 (15–95)	70 ± 22 (33–93)	67 ± 29 (15–95)	0.93

AET: Aerobic exercise training, SAT: Stretching and toning; CSQ: Client Satisfaction Questionnaire -8 rated on 1 (lowest satisfaction) to 4 (highest satisfaction); SUS: System Usability Scale rated from 1 (strongly disagree) to 5 (strongly agree). Total SUS score was calculated using the following formula: subtract 1 from the odd question answers, subtract the value of the even question answers from 5. Add up the total score, and multiple it by 2.5. Mann-Whitney U test was performed for group comparison.

Statistical Analysis

All statistical analysis were performed using SPSS 26.0 (IBM Corporation, Armonk, NY, 2011). Given the small sample size and nonparamedic distribution, Mann-Whitney U test was used for continuous variables and the Chi Square/Fisher’s Exact test for categorical variables to compare differences in clinical characteristics between AET and SAT groups. Two-way repeated measures analysis of variance (ANOVA) was used to analyze the interaction effort of group (AET versus SAT) and time (baseline versus 3-months), based on which marginal means and 95% confidence intervals were reported. The effect size of intervention programs on changes in 6WMT was calculated by Cohen’s d. The association between compliance and the improvement in 6WMT or potential confounding factors of compoliance (e.g. age, depression symptoms, sleep quality, baseline exercise endurance) was evaluated via Pearson correlations. Statistical significance was set as p < 0.05.

RESULTS:

Clinical characteristics of participants

A total of 114 individuals with cTBI were screened for eligibitiliy. Ninty-one were excluded and twenty-three eligible participants consented to the study (Figure 2). After consent, two failed medical screening due to abnormal electrocardiogram and one was lost to follow up after the initial visit. Characteristics of the 20 remaining participants are summarized in Table 1. The average age at the study was 58 year old (range 46–73) and the mean time after TBI was 6.2 years (range 1–16). As a group, the participants reported mild cognition symptoms (FIM cognition score 29.7 ± 5.4), mild depressive symptoms (PHQ9 score 8.1 ± 5.8), and poor sleep quality (PSQI score 7.5 ± 3.9). Ten of the participants were overweight (BMI 25 to 30) and an additional 7 were obese (BMI ≥ 30). All participants were under-active: 2 with RAPA score of 1 (rarely or never do any physical activities), 8 with RAPA score of 2 (do some light or moderate physical activity, but not every week), 6 with RAPA score of 3 (do some light physical activity every week), and 2 with RAPA score of 4 (do moderate physical activities every week, but < 30 minutes a day or 5 days a week).

Figure 2. Study flowchart.

AET: aerobic exercise training; SAT: stretching and toning.

Table 1.

Clinical Characteristics

	Total	AET Group	SAT Group	p
N (Female)	20 (3)	10 (2)	10 (1)	1.0
Demographic Characters
Age at the study (Y)	58.0 ± 9.3 (46–73)	58.1 ± 8.1 (47–69)	57.8 ± 10.9 (46–73)	0.91
Education	14.8 ± 2.5 (11–18)	15.8 ± 2.0 (14–18)	13.7 ± 2.5 (11–18)	0.052
iPphone user	12	9	3	0.02
Apple Watch User	6	3	3	1.0
TBI Characters
Age at the injury (Y)	51.8 ± 10.2 (36–70)	51.2 ± 8.5 (36–63)	52.3 ± 12.1 (40–70)	0.91
Time after TBI (Y)	6.2 ± 3.8 (1–16)	6.9 ± 3.0 (3–13)	5.5 ± 4.6 (1–16)	0.19
Physical Disability (Y)	10	3	7	0.18
RPQ (0–64)	18.9 ± 10.4 (5–40)	20.0 ± 11.5 (5–40)	17.7 ± 9.8 (5–34)	0.74
RPQ-Cognition (0–12)	6.3 ± 3.3 (0–12)	6.5 ± 3.8 (0–12)	6.0 ± 2.9 (2–11)	0.68
FIM Cognition (5–35)	29.7 ± 5.4 (19–35)	29.4 ± 4.6 (22–35)	29.9 ± 6.1 (19–35)	0.6
PHQ9 (0–27)	8.1 ± 5.8 (1–20)	7.4 ± 5.8 (1–18)	8.8 ± 6.1 (2–20)	0.58
PSQI (0–21)	7.5 ± 3.9 (1–17)	7.7 ± 4.1 (1–18)	8.8 ± 6.1 (2–20)	0.91
Fitness Status
RAPA (1–7)	2.6 ± 0.94 (1–4)	2.9 ± 0.88 (2–4)	2.3 ± 0.95 (1–4)	0.22
BMI (kg/m2)	29 ± 5 (17–38)	29 ± 5 (20–38)	29 ± 5 (17–36)	0.84
6 Min Walk Distance (m)	469 ± 125 (177–634)	528 ± 64 (422–634)	410 ± 147 (177 –561)	0.04

AET: Aerobic exercise training, SAT: Stretching and toning; TBI: Traumatic brain injury; RPQ: Rivermead Post-Concussion Symptom Questionnaire; FIM: Functional Independence Measure; PHQ-9: Patient Health Questionnaire-9; PSQI: Pittsburgh Sleep Quality Index; RAPA: Rapid Assessment of Physical Activity; BMI: Body Mass Index. Mann-Whitney U test was performed for group comparison.

These 20 participants (3 female, 17 male) were assigned to either AET (n=10) or SAT (n=10) group. Twelve participants (60%) already owned and used iPhones and half of them (6) also owned an Apple Watch. More participants in the AET group than in the SAT group owned an iPhone (9 in AET vs 3 in SAT, p < 0.01). Ten participants (3 in AET group and 7 in SAT group) had physical disabilities, including lower extremity weakness (n=4), limited range of hip motion (n=3), upper limb weakness (n=3), balance difficulty (n=2), hemianopia or monocular blindness (n=3), and upper limb ataxia (n=1).

Change in Exercise Endurance

After 3 months, the AET group trended towards maintaining exercise endurance when compared with the SAT group (0.33% vs −4%, p = 0.14) (Figure 2). The Cohen’s d for the interaction effect on exercise endurance was 0.43 which is equivalent to medium effect size. Five out of ten participants in AET group had improved exercise endurance with 4.6–7.1% increase of 6MWT and only 2 in SAT group had improvement in 6MWT (5 and 25.8%).

Feasibility and Usability Outcomes

Usability and compliance

All participants chose their preferred exercise activities in the program. Most participants (n=15, 75%) were able to use the TBICare app after one in-person training session. Three participants did not start the exercise program immediately and forgot how to use the TBICare app, requiring a second in-person training session to relearn the TBICare app. One participant (age 70 with FIM Cog score 12) could not use TBICare app independently, relying on his spouse. Only one participant (age 68 with FIM Cog score 12) and his spouse were not able to use the app consistently during the workout sessions because both forgot repeatedly to use the app.

All participants completed the 3-month exercise program and were able to exercise 30 minutes a day for 5 days a week at the end of the program. Twelve of 20 (60%) participants had an overall compliance rate higher than 80% (Figure 4). Six participants (30%) had compliance rates lower than 70%. Reported reasons for low compliance were: excessive fatigue, increased worktime to 70 hours a week in the last 4 weeks of the program, inability to use the TBICare appropriately during the workout session, and technology issues resulting in the loss of workout data (three participants). The mean compliance for all 20 participants was 76% (range 25% - 100%, median 80%). There was no significant difference in compliance between the AET and SAT groups (77% ± 18% vs 76% ± 24%, p = 0.95), between iPhone-users and non iPhone-users (76% ± 19% vs 76% ± 24%, p = 0.99), or between participants with and without physical disabilities (71% ± 24% vs 81% ± 19%, p = 0.26). No significant correlation was noted between compliance and age, reported severity of cognition symptoms on the FIM-Cog or RPQ-Cog, reported severity of depressive symptoms (PHQ9), sleep quality (PSQI), or baseline exercise endurance level (6MWT). No significant correlation was noted between compliance and improvement of 6MWT in either group.

Figure 4. Compliance in the exercise program.

Weekly compliance was calculated as the sum of all minutes of exercise during the week from completed exercise session divided by the total minutes prescribed that week. A completed session was defined as one that lasted longer than 10 minutes and in which the intensity target was achieved. The average weekly compliance rate was presented. AET: Aerobic exercise training; SAT: Stretching and toning; ns: non-significant defined as p> 0.05. Dashed line is the target compliance of 80%.

Adverse events

There were no severe study-related adverse events reported. The exercise program was paused and then restarted to complete a total of 12 weeks in 7 participants for the following reasons: COVID-19 infection (n=2), study-unrelated surgeries (n=3), shin pain (n=1), and fatigue (n=1).

Participant satisfaction

All participants completed the CSQ-8, and 90% of them rated the quality of our program as good or excellent (Table 2). Eighty-five percent of the participants were satisfied with the amount of help they received, and 75% reported that the program helped them to deal more effectively with their problems. Most participants (79%) reported that they received the service they wanted, that the program met most or almost all of their needs, and that they would recommend our program to their friends with similar needs. Most participants (85%) were satisfied with the service overall, and 80% would come back to our program. Overall satisfaction was similar between the two intervention groups (25 ± 5 vs 27 ±4, p = 0.28).

All participants except one also completed the SUS survey. Most participants (13 out of 19 participants) felt very confident using the TBICare app. However, four participants agreed that the TBICare app was unnecessarily complex, and five participants agreed with the statement, “I found TBICare app very cumbersome (awkward) to use”. In addition, five participants agreed that there was too much inconsistency in the TBICare app. Near half of participants (9) would like to use the TBICare app frequently. The average overall SUS score was 69 (range 15–95, SD 25), which was interpreted as average performance.²⁶ There was no difference in SUS overall scores between the AET and SAT groups (70 ± 22 vs 67 ± 29, p = 0.93) or between iPhone-users and non-iPhone-users (64 ± 25 vs 80 ± 18, p = 0.19)

During the semi-structured interview, all participants endorsed that the PMT-supported exercise training program was helpful, as the PMT was motivational (n=10, 50%) and a reminder to exercise (n=5, 25%). Most participants (90%) liked having a pre-set exercise schedule and goal; however, two participants (10%) considered this to be frustrating as they were scheduled to exercise at a time when they were experiencing pain or severe fatigue. Participants were almost equally split in reporting that the program was too easy or too challenging for them.

In terms of the learning of PMT mHealth system, most participants (60%) reported that the app were self-explanatory and easy to use. Only one participant reported that the PMT was a barrier to exercise. When participants had issues with the app, they contacted the research staff directly; only two referred to the provided user manual or video. Although we encouraged participants to communicate with the research team through the TBIChat in the PMT ecosystem, only two participants used this function. All participants chose to communicate with the research team through regular texting, as they were more familiar with it.

During both one-to-one interview and SUS survey, the most common complaint about the PMT system was app malfunction. Almost all participants had at least one workout session that did not register in the PMT system. The second most commonly reported problem was the lack of flexibility in the workout type and exercise schedule. Nearly half the participants wanted to have the ability to add new workout types in the prescription or to have a make-up date for scheduled exercise sessions. The most popular features in the TBICare app were the HR monitoring and the target HR and exercise duration reminders during the workout.

DISCUSSION:

This pilot study demonstrated that the PMT system appears to be a feasible and acceptable tool to promote compliance in an unsupervised exercise program for adults with cTBI. Specifically, we demonstrated that individuals with cTBI were able to use the PMT in community-based exercise programs with different exercise intensity levels and that participants were able to complete 76% of their assigned exercise duration with PMT support.

The success of any exercise training program relies heavily on individuals actually completing the prescribed exercise regimens, but doing so in an unsupervised setting is challenging. In a meta-analysis of 12 studies in a variety of populations with medical or musculoskeletal conditions, self-reported average compliance to exercise was only 67%.¹⁸ In our prior pilot study of adults with cTBI following the same exercise protocol as the current study (but with no PMT), only 40% of cTBI participants (8 out of 20) actually kept paper exercise logs as instructed, achieving 70% compliance overall.¹¹ In this study with the PMT, we were able to track the workout data in all the participants and observed a higher overall compliance of 76%, with 60% of participants achieving ≥ 80% compliance. This is particularly remarkable given that the current study sample only included participants with moderate to severe TBI history, unlike our previous pilot study that enrolled mostly those with mild TBI. Though direct comparisons cannot be made, the higher compliance in the current study with the PMT than the previous study without the PMT suggests that the PMT may, indeed, promote compliance to an exercise program in this population, though specific testing of its effectiveness in promoting compliance is warranted.

The PMT mHealth system can assist with goal-setting, self-monitoring, and remote coaching which all play import roles in increasing self-efficacy, self-management of symptoms, and overall psychological health, hence reducing known barriers to compliance.²⁸ A review of mHealth interventions for individuals with TBI concluded that mHealth systems like the PMT are promising approaches to support goal-setting, to monitor and reduce symptoms, and as effective compensatory strategies for cognitive impairment.²⁹ The feedback collected from our study participants also suggests that goal-setting and self-monitoring of HR and exercise duration were their two favorite features of our PMT-supported exercise program. The participants also reported that the reminder and support from the research team is very helpful, and felt “knowing someone is watching me all the time motivated me”.

We found that most individuals with cTBI were able to learn and use the TBICare app independently, however, some required either more in-person training or support from their families. In addition, the most common reason for the participants to contact research staff was app-related technical issues. Our experience suggests that implementation of the PMT in future exercise programs is feasible, but sufficient training and support (both individual and technical support) are vital. This is consistent with a recent review on mHealth interventions for individuals with TBI that indicated additional training and support for mHealth use may be necessary for individuals with TBI.²⁹

The PMT system is based on Apple Health app and monitor HR and activity using Apple Watch photoplethysmography technology. Apple Watch is the most popular commercially-available wearable device and carries relatively higher accuracy in HR and activity monitoring compared to other consumer- and research-grade wrist-worn wearable devices.^30–33 Different from other publicly available workout apps, the PMT system allows the participants to share their workout data with the trainer/research staff via a HIPPA-protected server. Compared to conventional self-reported exercise logs, the PMT allows us to directly track HR, exercise duration, and exercise frequency in real-time (during workouts). Therefore, we were able to remotely monitor compliance more objectively via the PMT than via paper logs. We could also initiate other interventions, such as coaching over the phone, video teleconference, or even in-person visits to address individual barriers to exercise when participants had difficulty initiating or completing their exercise sessions.

Our findings are all consistent with qualitative feedback from adults with TBI, their care partners, and clinicians who work with them regarding general use of mHealth apps to support their health, which indicated that participants want the ability to customize apps, saw promise in improving communication with healthcare providers, appreciated the cognitive strategies afforded by mHealth, noted the importance of app simplicity and accessibility, and emphasized the importance of user education.³⁴ The PMT addresses many of these areas, but further development and refinement is needed to best meet the needs and preferences of individuals with cTBI.

Despite promising improvement in compliance with exercise using the PMT system, we failed to demonstrate significant changes in exercise endurance in either groups. There are several possible explanations for this: 1) 3-month exercise program may be too short to improve 6MWT performance; 2) the self-paced 6MWT is a practical simple test to evaluate submaximal functional exercise capacity but not peak oxygen uptake and is not a sensitive measure to detect small differences between AET and SAT groups.²⁴ 3) many factors may contribute to the performance in 6MWT (prior nights’ sleep, fatigue, pain) which may also obscure small findings. In our prior study with the same exercise program, we demonstrated that AET tended to improve peak oxygen uptake when compared to SAT with a large effect size of 1.27.¹¹ Likely more formal cardiopulmonary exercise testing for fitness would be preferable in future studies.

Limitation and Future Direction

This study has several limitations. First, compliance data were compromised by app issues. Three of 20 participants were not able to register their workout regularly because of the instability of the TBICare app. Second, this study was conducted during the COVID-19 pandemic, so the exercise types and where they occurred may not reflect general exercise behaviors in non-pandemic situations. Third, the PMT system could only be used with an iPhone and Apple Watch. Therefore, generalization of its use to non-iPhone users in real-world is limited. In the future, more effort is needed to optimize the PMT system to: 1) enhance stablility to avoid loss of exercise data; 2) make it compatible with Android systems; 3) add more flexibility and customizable options; and 4) simplify the interface for the user.

CONCLUSIONS

With support from the care team, the PMT mHealth system could be an effective supplemental tool to support future exercise training programs. It allows the care team to closely and objectively monitor exercise progress, facilitates communication between participants and the care team, and potentially promotes good compliance in the exercise program. Further refinement of the PMT features and testing the proposed exercise program in a larger population is warranted to support the adoption of aerobic training in older person with chronic TBI.

Figure 3. Exercise Endurance Outcome.

The group difference between AET and SAT groups in exercise endurance was noted at baseline and after intervention. However, there was no significant improvements of exercise endurance in either group after 3-month intervention. 6MWD: 6-minute walk distance; AET: aerobic exercise training; SAT: stretching and toning.