Using Fitbit as an mHealth Intervention Tool to Promote Physical Activity: Potential Challenges and Solutions

Using Personalized Intervention Criteria in a Mobile Just-in-Time Adaptive Intervention for Increasing Physical Activity in University Students: Pilot Study

Background

While the health benefits of physical activity are well-known, adherence to regular physical activity remains a major challenge. Just-in-time adaptive intervention (JITAI) has been proposed as one method to increase physical activity by delivering an intervention at a time when individuals are more likely to make behavioral changes. However, most studies that have implemented JITAI have used uniform intervention criteria (UIC) across participants rather than personalized intervention criteria (PIC) for the individual.

Objective

The objective of this paper was to examine the effectiveness of using JITAI implemented with PIC to increase physical activity.

Methods

Healthy university students wore a wrist activity monitor for 2 weeks. Participants were divided into 2 groups, which received JITAI to promote physical activity according to either PIC or UIC. In the first week, the mean distance moved and sedentary time per hour for each participant were calculated to derive PIC. UIC was obtained from a 2-week study with a different sample (n=47) conducted under the same conditions. In the second week, JITAI prompts were sent every hour if both of the following criteria were met: the distance moved was shorter, and sedentary time was longer than PIC or UIC. Differences in changes in physical activity as a result of implementing interventions according to PIC and UIC were analyzed using multilevel models.

Results

We analyzed data from 28 healthy university students (18-23 y old, female n=12). Both PIC (P<.001) and UIC (P<.001) significantly increased physical activity in the first hour after JITAI was received. In that first hour, PIC increased physical activity more than UIC; more calories were burned (P=.02), more steps were taken (P=.007), and distance moved was increased (P=.003). However, over the course of the week, the use of JITAI did not significantly increase physical activity levels.

Conclusions

Our results appear to suggest that PIC-based JITAI is more effective than UIC-based JITAI, consistent with the idea of a need for precision health approaches. Further research is needed to develop effective long-term intervention designs with sustainable effects.

Sensor-triggered ecological momentary assessment in physical activity and sedentary behaviour research among Belgian community-dwelling elderly: lessons learnt from intensive longitudinal studies

OBJECTIVES

Regular physical activity (PA) and reduced sedentary behaviour (SB) have been associated with positive health outcomes, but many older adults do not comply with the current recommendations. Sensor-triggered ecological momentary assessment (EMA) studies allow capturing real-time data during or immediately after PA or SB, which can yield important insights into these behaviours. Despite the promising potential of sensor-triggered EMA, this methodology is still in its infancy. Addressing methodological challenges in sensor-triggered EMA studies is essential for improving protocol adherence and enhancing validity. Therefore, this study aimed to examine (1) the patterns in sensor-triggered EMA protocol adherence (eg, compliance rates), (2) the impact of specific settings (eg, event duration) on the number of prompted surveys, and (3) participants' experiences with engaging in a sensor-triggered EMA study.

DESIGN

Two longitudinal, sensor-triggered EMA studies-one focused on PA and the other on SB-were conducted using similar methodologies from February to October 2022. Participants' steps were monitored for seven days using a Fitbit activity tracker, which automatically prompted an EMA survey through the HealthReact smartphone application when specified (in)activity thresholds were reached. After the monitoring period, qualitative interviews were conducted. Data from both studies were merged.

SETTING

The studies were conducted among community-dwelling Belgian older adults.

PARTICIPANTS

The participants had a median age of 72 years, with 54.17% being females. The PA study included 88 participants (four dropped out), while the SB study included 76 participants (seven dropped out).

PRIMARY AND SECONDARY OUTCOME MEASURES

Descriptive methods and generalised logistic mixed models were employed to analyse EMA adherence patterns. Simulations were conducted to assess the impact of particular settings on the number of prompted EMA surveys. Additionally, qualitative interview data were transcribed verbatim and thematically analysed using NVivo.

RESULTS

Participants responded to 81.22% and 79.10% of the EMA surveys in the PA and SB study, respectively. The confirmation rate, defined as the percentage of EMA surveys in which participants confirmed the detected behaviour, was 94.16% for PA and 72.40% for SB. Logistic mixed models revealed that with each additional day in the study, the odds of responding to the EMA survey increased significantly by 1.59 times (OR=1.59, 95% CI: 1.36 to 1.86, p<0.01) in the SB study. This effect was not observed in the PA study. Furthermore, time in the study did not significantly impact the odds of participants confirming to be sedentary (OR=0.97, 95% CI: 0.92 to 1.02, p=0.28). However, it significantly influenced the odds of confirming PA (OR: 0.81, 95% CI: 0.68 to 0.97, p=0.02), with the likelihood of confirming decreasing by 19% with each additional day in the study. Furthermore, a one-minute increase in latency (ie, time between last syncing and starting the EMA survey) in the PA study decreased the odds of the participant confirming to be physically active by 20% (OR: 0.80, 95% CI: 0.72 to 0.89, p<0.01). Simulations of the specific EMA settings revealed that reducing the event duration and shorter minimum time intervals between prompts increased the number of EMA surveys. Overall, most participants found smartphone usage to be feasible and rated the HealthReact app as user-friendly. However, some reported issues, such as not hearing the notification, receiving prompts at an inappropriate time and encountering technical issues. While the majority reported that their behaviour remained unchanged due to study participation, some noted an increased awareness of their habits and felt more motivated to engage in PA.

CONCLUSIONS

This study demonstrates the potential of sensor-triggered EMA to capture real-time data on PA and SB among older adults, showing strong adherence potential with compliance rates of approximately 80%. The SB study had lower confirmation rates than the PA study, due to technical issues and discrepancies between self-perception and device-based measurements. Practical recommendations were provided for future studies, including improvements in survey timing, technical reliability and strategies to reduce latency.

Patient-Centered mHealth Intervention to Improve Self-Care in Patients With Chronic Heart Failure: Phase 1 Randomized Controlled Trial

BACKGROUND

Heart failure (HF) is one of the most common causes of hospital readmission in the United States. These hospitalizations are often driven by insufficient self-care. Commercial mobile health (mHealth) technologies, such as consumer-grade apps and wearable devices, offer opportunities for improving HF self-care, but their efficacy remains largely underexplored.

OBJECTIVE

The objective of this study was to examine the feasibility, acceptability, safety, and preliminary efficacy of a patient-centered mHealth intervention (iCardia4HF) that integrates 3 consumer mHealth apps and devices (Heart Failure Health Storylines, Fitbit, and Withings) with a program of individually tailored SMS text messages to improve HF self-care.

METHODS

We conducted a phase 1 randomized controlled trial. Eligible patients had stage C HF, were aged ≥40 years, and had New York Heart Association (NYHA) class I, II, or III HF. Patients were randomly assigned to either iCardia4HF plus usual care or to usual care only and were observed for 8 weeks. Key feasibility measures were recruitment and retention rates. The primary efficacy outcome was change in HF self-care subscale scores (maintenance, symptom perception, and self-care management) at 8 weeks, assessed with the Self-Care Heart Failure Index (SCHFI; version 7.2). Key secondary outcomes were modifiable behaviors targeted by the intervention (health beliefs, self-efficacy, and HF knowledge), health status, and adherence to daily self-monitoring of 2 core vital signs (body weight and blood pressure).

RESULTS

A total of 27 patients were enrolled in the study and randomly assigned to iCardia4HF (n=13, 48%) or usual care (n=14, 52%). Of these 27 patients, 11 (41%) in the intervention group (iCardia4HF) and 14 (52%) in the usual care group started their assigned care and were included in the full analysis. Patients' mean age was 56 (SD 8.3) years, 44% (11/25) were female, 92% (23/25) self-reported race as Black, 76% (19/25) had NYHA class II or III HF, and 60% (15/25) had HF with reduced left ventricular ejection fraction. Participant retention, completion of study visits, and adherence to using the mHealth apps and devices for daily self-monitoring were high (>80%). At 8 weeks, the mean group differences in changes in the SCHFI subscale scores favored the intervention over the control group: maintenance (Cohen d=0.19, 95% CI -0.65 to 1.02), symptom perception (Cohen d=0.33, 95% CI -0.51 to 1.17), and self-care management (Cohen d=0.25, 95% CI -0.55 to 1.04). The greatest improvements in terms of effect size were observed in self-efficacy (Cohen d=0.68) and health beliefs about medication adherence (Cohen d=0.63) and self-monitoring adherence (Cohen d=0.94). There were no adverse events due to the intervention.

CONCLUSIONS

iCardia4HF was found to be feasible, acceptable, and safe. A larger trial with a longer follow-up duration is warranted to examine its efficacy among patients with HF.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03642275; https://clinicaltrials.gov/study/NCT03642275.

Using a Wearable Brain Activity Sensing Device in the Treatment of Long COVID Symptoms in an Open-Label Clinical Trial

OBJECTIVE

This study evaluated the feasibility and satisfaction of using a wearable brain activity sensing device for stress reduction among patients experiencing Long COVID (LC).

PATIENTS AND METHODS

Patients with LC (N = 45) were invited to participate in an open-label pilot study. Participants were asked to use a brain-sensing electroencephalogram (S-EEG) wearable device (Muse-S™) daily for 90 days and followed for an additional 90 days (180 days total participation). Study enrollment began June 28, 2022, and ended July 28, 2023.

RESULTS

Patients were predominantly female (82%), white (96%), and with an average age of 47.6 (±12.5) years. At the end of treatment (90 days), cognition, sleep, self-efficacy, and quality-of-life quality of life improved (P < .001 to P = .04). In addition, stress and anxiety were significantly reduced (P < .001). These significant changes persisted by end-of-study (180 days). For stress (P = .023) and anxiety (P = .033), a change from the baseline during follow-up was significantly associated with the amount of S-EEG usage. The end-of-study satisfaction survey indicated that patients were very satisfied with the S-EEG device, and 97% planned to continue using it.

CONCLUSION

Results suggest that the S-EEG device improved cognition in LC, which is one of the main severe symptoms we see in our LC clinic. In addition, it helped reduce stress and anxiety. These findings have important clinical implications for LC patients using S-EEG as an adjunct to concurrent LC clinical care which includes pharmacotherapies.

Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model

BACKGROUND

Wrist-worn data from commercially available devices has potential to characterize sedentary time for research and for clinical and public health applications. We propose a model that utilizes heart rate in addition to step count data to estimate the proportion of time spent being sedentary and the usual length of sedentary bouts.

METHODS

We developed and trained two Hidden semi-Markov models, STEPHEN (STEP and Heart ENcoder) and STEPCODE (STEP enCODEr; a steps-only based model) using consumer-grade Fitbit device data from participants under free living conditions, and validated model performance using two external datasets. We used the median absolute percentage error (MDAPE) to measure the accuracy of the proposed models against research-grade activPAL device data as the referent. Bland-Altman plots summarized the individual-level agreement with activPAL.

RESULTS

In OPTIMISE cohort, STEPHEN's estimates of the proportion of time spent sedentary had significantly (p < 0.001) better accuracy (MDAPE [IQR] = 0.15 [0.06-0.25] vs. 0.23 [0.13-0.53)]) and agreement (Bias Mean [SD]=-0.03[0.11] vs. 0.14 [0.11]) than the proprietary software, estimated the usual sedentary bout duration more accurately (MDAPE[IQR] = 0.11[0.06-0.26] vs. 0.42[0.32-0.48]), and had better agreement (Bias Mean [SD] = 3.91[5.67] minutes vs. -11.93[5.07] minutes). With the ALLO-Active dataset, STEPHEN and STEPCODE did not improve the estimation of proportion of time spent sedentary, but STEPHEN estimated usual sedentary bout duration more accurately than the proprietary software (MDAPE[IQR] = 0.19[0.03-0.25] vs. 0.36[0.15-0.48]) and had smaller bias (Bias Mean[SD] = 0.70[8.89] minutes vs. -11.35[9.17] minutes).

CONCLUSIONS

STEPHEN can characterize the proportion of time spent being sedentary and usual sedentary bout length. The methodology is available as an open access R package available from https://github.com/limfuxing/stephen/ . The package includes trained models, but users have the flexibility to train their own models.

Applications of wearable activity monitors for prostate cancer survivors: A systematic scoping review

BACKGROUND

Wearable technology is used to monitor and motivate physical activity (PA) and provides continuous, objective PA and sleep data outside the clinical setting. We reviewed the literature to understand how wearables are integrated into prostate cancer (PC) investigations in order to identify current practices, gaps, and research opportunities.

METHODS

We conducted a literature search for articles using wearables, among PC survivors published between 2012 and 2022. We extracted study details, interventions and outcomes, participant baseline characteristics, and device characteristics and grouped them by study type: randomized control trials (RCTs) and non-randomized studies.

RESULTS

Of 354 articles screened, 44 met eligibility criteria (23 RCTs, and 21 non-randomized). 89% used wearables to monitor PA metrics, 11%, sleep metrics, and 6.8%, both. Most studies involved exercise (70% RCTs, 9% non-randomized studies) or lifestyle interventions (30% RCTs, 9% non-randomized studies). Intervention delivery methods included personalized computer-based (48%), in-person (e.g., trainer) (20%), and education web or print-based (20%). Interventions occurred at the participant's home (48%) or at a gym (20%). 57% of the studies evaluated the feasibility and acceptability of the wearable as an activity-measuring device or as part of a remotely delivered computer-based intervention. Studies used wearables to monitor adherence to PA interventions, motivate behavior change, to assess patient outcomes (e.g., patient function, quality of life, mood), or as data collection tools.

CONCLUSIONS

Wearables are primarily being used to assess daily activity and monitor adherence to exercise interventions in clinical studies involving PC survivors. Findings suggest that they are feasible for use in this population. More research is needed to understand how to integrate wearables into routine clinical care, expand their use to predict clinical outcomes, or to deliver tailored interventions for PC survivors.

Mitigating data quality challenges in ambulatory wrist-worn wearable monitoring through analytical and practical approaches

Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable technologies, particularly wrist-worn devices, offer promising solutions for longitudinal patient monitoring, replacing subjective, intermittent self-reporting with objective, continuous monitoring. However, collecting and analyzing data from wearables presents several challenges, such as data entry errors, non-wear periods, missing data, and wearable artifacts. In this work, we explore these data analysis challenges using two real-world datasets (mBrain21 and ETRI lifelog2020). We introduce practical countermeasures, including participant compliance visualizations, interaction-triggered questionnaires to assess personal bias, and an optimized pipeline for detecting non-wear periods. Additionally, we propose a visualization-oriented approach to validate processing pipelines using scalable tools such as tsflex and Plotly-Resampler. Lastly, we present a bootstrapping methodology to evaluate the variability of wearable-derived features in the presence of partially missing data segments. Prioritizing transparency and reproducibility, we provide open access to our detailed code examples, facilitating adaptation in future wearable research. In conclusion, our contributions provide actionable approaches for improving wearable data collection and analysis.

Exploring Pain Reduction through Physical Activity: A Case Study of Seven Fibromyalgia Patients

Fibromyalgia is a chronic disease that affects a considerable fraction of the global population, primarily women. Physical activity is often recommended as a tool to manage the symptoms. In this study, we tried to replicate a positive result of pain reduction through physical activity. After collecting pain and physical activity data from seven women with fibromyalgia, one patient experienced a considerable reduction in pain intensity. According to the patient, the improvement was related to physical activity. Our study was conducted to investigate the replicability of this result through personalized activity recommendations. Out of the other six patients, three experienced a reduction in pain. The remaining three patients did not experience any pain relief. Our results show that two of these were not able to follow the activity recommendations. These results indicate that physical activity may have a positive effect on chronic pain patients. To estimate how effective physical activity can be for this patient group, an intervention with longer follow-ups and larger sample sizes needs to be performed in the future.

Smart Medical Evacuation Support System for the Military

Medical support in crisis situations is a major challenge. Efficient implementation of the medical evacuation process especially in operations with limited human resources that may occur during armed conflicts can limit the loss of these resources. Proper evacuation of wounded soldiers from the battlefield can increase the chances of their survival and rapid return to further military operations. This paper presents the technical details of the decision support system for medical evacuation to support this process. The basis for the functioning of this system is the continuous measurement of vital signs of soldiers via a specialized measurement module with a set of medical sensors. Vital signs values are then transmitted via the communication module to the analysis and inference module, which automatically determines the color of medical triage and the soldier's chance of survival. This paper presents the results of tests of our system to validate it, which were carried out using test vectors of soldiers' vital signs, as well as the results of the system's performance on a group of volunteers who performed typical activities of tactical operations. The results of this study showed the usefulness of the developed system for supporting military medical services in military operations.

Feasibility of Fit24, a Digital Diabetes Prevention Program for Hispanic Adolescents: Qualitative Evaluation Study

BACKGROUND

Digital health interventions are promising for reaching and engaging high-risk youth in disease prevention opportunities; however, few digital prevention interventions have been developed for Hispanic youth, limiting our knowledge of these strategies among this population.

OBJECTIVE

This study qualitatively assessed the feasibility and acceptability of Fit24, a 12-week goal-setting intervention that uses a Fitbit watch (Fitbit Inc) and theoretically grounded SMS text messages to promote physical activity and sleep among Hispanic adolescents (aged between 14 and 16 years) with obesity.

METHODS

After completing the intervention, a subsample of youth (N=15) participated in an in-depth interview. We categorized the themes into dimensions based on participant perspectives using the Practical, Robust Implementation, and Sustainability Model (PRISM) framework.

RESULTS

Participants shared positive perceptions of wearing the Fitbit and receiving SMS text messages. Youth were highly engaged in monitoring their behaviors and perceived increased activity and sleep. Almost all youth organically received social support from a peer or family member and suggested the use of a group chat or team challenge for integrating peers into future interventions. However, most youth also expressed the need to take personal responsibility for the change in their behavior. Barriers that impacted the feasibility of the study included the skin-irritating material on the Fitbit watch band and environmental barriers (eg, lack of resources and school schedules), that limited participation in activity suggestions. Additionally, sync issues with the Fitbit limited the transmission of data, leading to inaccurate feedback.

CONCLUSIONS

Fit24 is a promising approach for engaging Hispanic youth in a diabetes prevention program. Strategies are needed to address technical issues with the Fitbit and environmental issues such as message timing. While integrating peer social support may be desired by some, peer support strategies should be mindful of youth's desire to foster personal motivation for behavior change. Findings from this study will inform future diabetes prevention trials of Fit24 and other digital health interventions for high-risk pediatric populations.

Randomized Controlled Trial of Aerobic Exercise for Smoking Cessation Among Individuals With Elevated Depressive Symptoms

INTRODUCTION

Adults with depression have higher rates of cigarette smoking and are more likely to relapse than those without depression. Pharmacological, psychological, and combined interventions have largely yielded small improvements in smoking outcomes for adults with depression. Aerobic exercise (AE) may facilitate smoking cessation in this subpopulation.

METHODS

This study was a 12-week two-arm randomized controlled trial that evaluated the effect of a moderate-intensity AE program compared to a health education contact (HEC) control on smoking cessation in adults with elevated depressive symptoms (mild to severe). Participants (n = 231) were randomized to AE or HEC and received smoking cessation treatment (telephone counseling and nicotine replacement therapy). Primary (biologically confirmed 7-day point prevalence abstinence) and secondary (depressive symptoms, objective and self-reported physical activity, and cardiorespiratory fitness) outcomes were assessed at baseline, 3, 6, and 12 months. Data were analyzed with mixed-effects generalized linear models controlling for age, gender, nicotine dependence, history of major depression disorder, and month of follow-up assessment.

RESULTS

There were no significant differences in primary or secondary outcomes between the AE and HEC groups.

CONCLUSIONS

The AE program was not superior to HEC in facilitating smoking cessation, increases in physical activity, or improved depressive symptoms. Given evidence for the positive acute effects of exercise on mood and smoking urges, future research should consider testing alternative exercise approaches for aiding smoking cessation beyond structured, AE programs.

IMPLICATIONS

This study found that an adjunctive aerobic exercise (AE) program was not superior to a health education contact control for adults with elevated depressive symptoms, all of whom also received standard smoking cessation treatment. This finding adds to the growing body of literature that structured AE programs for smoking cessation may have limited efficacy for cessation outcomes. Future research is needed to test alternative methods of integrating AE into smoking cessation treatment, such as strategically using exercise to manage cravings and low mood in the moment.

Family-Focused Facilitated Fitness: Feasibility and Acceptability of a Couple-Based Physical Activity Intervention for Hematopoietic Cell Transplant Recipients and Their Caregiving Partners

Reductions in physical activity (PA) are common among patients following hematopoietic cell transplantation, and a risk factor for poor physical functioning. PA among spouses/cohabiting partners, who frequently serve as the patient's primary caregiver, may also be reduced due to caregiving demands and limited bandwidth for self-care. In addition, the patient-caregiver relationship can be compromised, and communication patterns disrupted. All PA interventions in the hematopoietic cell transplantation setting have focused entirely on patients, ignoring an opportunity to synergistically engage and benefit the caregiver as well. We sought to test feasibility and acceptability of a couple-based intervention entitled Family-Focused Facilitated Fitness (FFFF), designed to improve PA as assessed by daily step counts among both patients undergoing hematopoietic cell transplantation and their caregivers. Guided by interdependence and communal coping perspectives, FFFF is an 8-week, remotely-delivered intervention that provides training in communication skills and joint problem-solving to help patients and caregivers support one another in PA. Participants are also given a Fitbit to track their steps and weekly individualized step prescription based on the 75th percentile ranked value of their last 7 recorded daily step counts. A priori benchmarks for feasibility and acceptability in this single-arm pilot were as follows: 50% of eligible couples would agree to participate, 70% of couples would attend all 8 sessions, 80% of participants would provide valid Fitbit wear data 4/7 days/ week, and among sessions reviewed for treatment fidelity, 85% of treatment protocol elements would be covered. Couples were recruited prior to transplant. Among 26 couples approached and deemed eligible, 17 enrolled (65% agreement) and completed baseline assessment. Three couples later withdrew after transplantation but prior to receiving the intervention, resulting in 14 couples commencing the intervention, on average 21 days post-transplant. Four couples subsequently discontinued due to medical complications (n = 3) or caregiver schedule (n = 1). Among the 10 couples who completed the intervention, the percentage of participants meeting our benchmark of valid Fitbit wear at least 4 days per week was 85% in week 1, 90% in weeks 2 to 7, and 80% in week 8. Treatment fidelity was 95% on average across 24 sessions reviewed (3 cases). Treatment satisfaction scores were uniformly high across multiple dimensions, with all means above 4 on the 1 to 5 scale. Daily step counts among those attending all 8 intervention sessions increased from 2249 ± 302 steps/day in week 1 to 4975 ± 1377 steps/day in week 8 among patients, and from 8676 ± 3760 steps/day in week 1 to 9838 ± 3723 steps/day in week 8 among caregivers. Qualitative feedback indicated perceived mental and physical health benefits of the program. Participants also offered suggestions for adaptations to accommodate medical setbacks and constraints. All a priori feasibility benchmarks were met or exceeded. Results offer promise for utility of the program to engage and leverage patient-caregiver dyads to increase PA following transplant. An investigation using a randomized controlled design will be necessary to adequately examine change over time relative to control and its possible impact on clinical and patient-reported outcomes.

Effect of Intrapersonal and Interpersonal Behavior Change Strategies on Physical Activity Among Older Adults: A Randomized Clinical Trial

Importance

Despite guidelines that recommend physical activity (PA), little is known about which types of behavior change strategies (BCSs) effectively promote sustained increases in PA in older adults who are insufficiently active.

Objective

To determine whether intrapersonal BCSs (eg, goal setting) or interpersonal BCSs (eg, peer-to-peer sharing or learning) combined with the Otago Exercise Program (17 strength and balance exercises and a walking program that are learned and individually tailored, with instruction to perform 3 times per week at home or location of choice) and a wearable PA monitor help older adults sustain increases in their PA.

Design, Setting, and Participants

This 2 × 2 factorial randomized clinical trial (Community-Based Intervention Effects on Older Adults' Physical Activity) of community-dwelling older adults 70 years or older with PA levels below minimum national PA guidelines was conducted in urban community centers. Dates of enrollment were from November 17, 2017, to June 15, 2021, with final follow-up assessments completed on September 2, 2022.

Interventions

Participants were randomized to intrapersonal (eg, goal setting) BCSs, interpersonal (eg, problem-solving with peer-to-peer sharing and learning) BCSs, intrapersonal and interpersonal BCSs, or an attention control group. All interventions included a PA monitor and 8 weekly small-group meetings with discussion, practice, and instructions to implement the exercise program and relevant BCSs independently between meetings and after the intervention.

Main Outcomes and Measures

The primary outcome was daily minutes of objectively measured total PA (light, moderate, or vigorous intensities) averaged over 7 to 10 days, measured at baseline and after the intervention at 1 week, 6 months, and 12 months.

Results

Among 309 participants (mean [SD] age, 77.4 [5.0] years; 240 women [77.7%]), 305 (98.7%) completed the intervention, and 302 (97.7%) had complete data. Participants receiving PA interventions with interpersonal BCS components exhibited greater increases in total PA than did those who did not at 1 week (204 vs 177 PA minutes per day; adjusted difference, 27.1 [95% CI, 17.2-37.0]; P < .001), 6 months (195 vs 175 PA minutes per day; adjusted difference, 20.8 [95% CI, 10.0-31.6]; P < .001), and 12 months (195 vs 168 PA minutes per day; adjusted difference, 27.5 [95% CI, 16.2-38.8]; P < .001) after the intervention. Compared with participants who did not receive interventions with intrapersonal BCS components, participants who received intrapersonal BCSs exhibited no significant changes in total PA at 1 week (192 vs 190 PA minutes per day; adjusted difference, 1.8 [95% CI, -8.6 to 12.2]; P = .73), 6 months (183 vs 187 PA minutes per day; adjusted difference, -3.9 [95% CI, -15.0 to 7.1]; P = .49), or 12 months (177 vs 186 PA minutes per day; adjusted difference, -8.8 [95% CI, -20.5 to 2.9]; P = .14) after the intervention. Interactions between intrapersonal and interpersonal BCSs were not significant.

Conclusions and Relevance

In this randomized clinical trial, older adults with low levels of PA who received interpersonal BCSs, the exercise program, and a PA monitor exhibited significant increases in their PA for up to 12 months after the intervention. Intrapersonal BCSs elicited no significant PA changes and did not interact with interpersonal BCSs. Our findings suggest that because effects of a PA intervention on sustained increases in older adults' PA were augmented with interpersonal but not intrapersonal BCSs, approaches to disseminating and implementing the intervention should be considered.

Trial Registration

ClinicalTrials.gov Identifier: NCT03326141.

Fitbit's accuracy to measure short bouts of stepping and sedentary behaviour: validation, sensitivity and specificity study

Objective

This study aims to assess the suitability of Fitbit devices for real-time physical activity (PA) and sedentary behaviour (SB) monitoring in the context of just-in-time adaptive interventions (JITAIs) and event-based ecological momentary assessment (EMA) studies.

Methods

Thirty-seven adults (18-65 years) and 32 older adults (65+) from Belgium and the Czech Republic wore four devices simultaneously for 3 days: two Fitbit models on the wrist, an ActiGraph GT3X+ at the hip and an ActivPAL at the thigh. Accuracy measures included mean (absolute) error and mean (absolute) percentage error. Concurrent validity was assessed using Lin's concordance correlation coefficient and Bland-Altman analyses. Fitbit's sensitivity and specificity for detecting stepping events across different thresholds and durations were calculated compared to ActiGraph, while ROC curve analyses identified optimal Fitbit thresholds for detecting sedentary events according to ActivPAL.

Results

Fitbits demonstrated validity in measuring steps on a short time scale compared to ActiGraph. Except for stepping above 120 steps/min in older adults, both Fitbit models detected stepping bouts in adults and older adults with sensitivities and specificities exceeding 87% and 97%, respectively. Optimal cut-off values for identifying prolonged sitting bouts achieved sensitivities and specificities greater than 93% and 89%, respectively.

Conclusions

This study provides practical insights into using Fitbit devices in JITAIs and event-based EMA studies among adults and older adults. Fitbits' reasonable accuracy in detecting short bouts of stepping and SB makes them suitable for triggering JITAI prompts or EMA questionnaires following a PA or SB event of interest.

An open-source system for efficient clinical trial support: The COMET study experience

Exercise clinical trials are complex, logistically burdensome, and require a well-coordinated multi-disciplinary approach. Challenges include managing, curating, and reporting on many disparate information sources, while remaining responsive to a variety of stakeholders. The Combined Exercise Trial (COMET, NCT04848038) is a one-year comparison of three exercise modalities delivered in the community. Target enrollment is 280 individuals over 4 years. To support rigorous execution of COMET, the study team has developed a suite of scripts and dashboards to assist study stakeholders in each of their various functions. The result is a highly automated study system that preserves rigor, increases communication, and reduces staff burden. This manuscript describes system considerations and the COMET approach to data management and use, with a goal of encouraging further development and adaptation by other study teams in various fields.

Technical Assistance Received by Older Adults to Use Commercially Available Physical Activity Monitors (Ready Steady 3.0 Trial): Ad-Hoc Descriptive Longitudinal Study

Background

Despite evidence that regular physical activity (PA) among older adults confers numerous health and functional benefits, PA participation rates are low. Using commercially available wearable PA monitors (PAMs) is one way to augment PA promotion efforts. However, while expert recommendations exist for the specific information needed at the beginning of PAM ownership and the general ongoing need for structures that support as-needed technical troubleshooting, information is lacking about the type, frequency, and modes of assistance needed during initial and long-term ownership.

Objective

This paper describes problems reported and technical assistance received by older adults who used PAMs during the 18 months they participated in a community-based PA trial: Ready Steady 3.0 (RS3).

Methods

This was an ad-hoc longitudinal analysis of process variables representing technical problems reported and assistance received by 113 RS3 study participants in the 18 months after their orientation to PAMs. Variables included date of contact, problem(s) reported, mode of technical assistance, and whether the equipment was replaced. The descriptive analysis included frequencies and incidence rates of distinct contacts, types of problems, and technical assistance modes.

Results

On average, participants were aged 77 (SD 5.2) years. Most identified as female (n=87, 77%), reported experience using smartphones (n=92, 81.4%), and used the PAM between 2 and 18 months. Eighty-two participants (72.6%) reported between 1 to 9 problems with using PAMs, resulting in a total of 150 technical assistance contacts with a mean of 1.3 (SD 1.3) contacts. The incidence rate of new, distinct contacts for technical assistance was 99 per 100 persons per year from 2018 to 2021. The most common problems were wearing the PAM (n=43, 28.7%), reading its display (n=23, 15.3%), logging into its app (n=20, 13.3%), charging it (n=18, 12%), and synchronizing it to the app (n=16, 10.7%). The modalities of technical assistance were in person (n=53, 35.3%), by telephone (n=51, 34%), by email (n=25, 16.7%), and by postal mail (n=21, 14%).

Conclusions

In general, the results of this study show that after receiving orientation to PAMs, problems such as uncomfortable wristbands, difficulty using the PAM or its related app, and obtaining or interpreting relevant personal data were occasionally reported by participants in RS3. Trained staff helped participants troubleshoot and solve these technical problems primarily in person or by phone. Results also underscore the importance of involving older adults in the design, usability testing, and supportive material development processes to prevent technical problems for the initial and ongoing use of PAMs. Clinicians and researchers should further assess technical assistance needed by older adults, accounting for variations in PAM models and wear time, while investigating additional assistance strategies, such as proactive support, short GIF videos, and video calls.

Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data

BACKGROUND

Patient-generated health data are important in the management of several diseases. Although there are limitations, information can be obtained using a wearable device and time-related information such as exercise time or sleep time can also be obtained. Fitbits can be used to acquire sleep onset, sleep offset, total sleep time (TST), and wakefulness after sleep onset (WASO) data, although there are limitations regarding the depth of sleep and satisfaction; therefore, the patient's subjective response is still important information that cannot be replaced by wearable devices.

OBJECTIVE

To effectively use patient-generated health data related to time such as sleep, it is first necessary to understand the characteristics of the time response recorded by the user. Therefore, the aim of this study was to analyze the characteristics of individuals' time perception in comparison with wearable data.

METHODS

Sleep data were acquired for 2 weeks using a Fitbit. Participants' sleep records were collected daily through chatbot conversations while wearing the Fitbit, and the two sets of data were statistically compared.

RESULTS

In total, 736 people aged 30-59 years were recruited for this study, and the sleep data of 543 people who wore a Fitbit and responded to the chatbot for more than 7 days on the same day were analyzed. Research participants tended to respond to sleep-related times on the hour or in 30-minute increments, and each participant responded within the range of 60-90 minutes from the value measured by the Fitbit. On average for all participants, the chat responses and the Fitbit data were similar within a difference of approximately 15 minutes. Regarding sleep onset, the participant response was 8 minutes and 39 seconds (SD 58 minutes) later than that of the Fitbit data, whereas with respect to sleep offset, the response was 5 minutes and 38 seconds (SD 57 minutes) earlier. The participants' actual sleep time (AST) indicated in the chat was similar to that obtained by subtracting the WASO from the TST measured by the Fitbit. The AST was 13 minutes and 39 seconds (SD 87 minutes) longer than the time WASO was subtracted from the Fitbit TST. On days when the participants reported good sleep, they responded 19 (SD 90) minutes longer on the AST than the Fitbit data. However, for each sleep event, the probability that the participant's AST was within ±30 and ±60 minutes of the Fitbit TST-WASO was 50.7% and 74.3%, respectively.

CONCLUSIONS

The chatbot sleep response and Fitbit measured time were similar on average and the study participants had a slight tendency to perceive a relatively long sleep time if the quality of sleep was self-reported as good. However, on a participant-by-participant basis, it was difficult to predict participants' sleep duration responses with Fitbit data. Individual variations in sleep time perception significantly affect patient responses related to sleep, revealing the limitations of objective measures obtained through wearable devices.

Effects of personalized exercise prescriptions and social media delivered through mobile health on cancer survivors' physical activity and quality of life

PURPOSE

This study aimed to examine the effects of a multi-component mobile health intervention (wearable, apps, and social media) on cancer survivors' (CS') physical activity (PA), quality of life, and PA determinants compared to exercise prescription only, social media only, and attention control conditions.

METHODS

A total of 126 CS (age = 60.37 ± 7.41 years, mean ± SD) were recruited from the United States. The study duration was 6 months and participants were randomly placed into 4 groups. All participants received a Fitbit tracker and were instructed to install its companion app to monitor their daily PA. They (1) received previously established weekly personalized exercise prescriptions via email, (2) received weekly Facebook health education and interacted with one another, (3) received both Conditions 1 and 2, or (4) were part of the control condition, meaning they adopted usual care. CS PA daily steps, quality of life (i.e., physical health and mental health), and PA determinants (e.g., self-efficacy, social support) were measured at baseline, 3 months, and 6 months.

RESULTS

The final sample size included 123 CS. The results revealed only the multi-component condition had greater improvements in PA daily steps than the control condition post-intervention (95% confidence interval (95%CI): 368-2951; p < 0.05). Similarly, those in the multi-component condition had significantly greater increased physical health than the control condition (95%CI: -0.41 to -0.01; p < 0.05) over time. In addition, the social media condition had significantly greater increased perceived social support than the control condition (95%CI: 0.01-0.93; p < 0.05). No other significant differences on outcomes were identified.

CONCLUSION

The study findings suggest that the implementation of a multi-component mobile health intervention had positive effects on CS PA steps and physical health. Also, offering social media intervention has the potential to improve CS perceived social support.

Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions

BACKGROUND

Just-in-time adaptive interventions (JITAIs) are designed to provide support when individuals are receptive and can respond beneficially to the prompt. The notion of a just-in-time (JIT) state is critical for JITAIs. To date, JIT states have been formulated either in a largely data-driven way or based on theory alone. There is a need for an approach that enables rigorous theory testing and optimization of the JIT state concept.

OBJECTIVE

The purpose of this system ID experiment was to investigate JIT states empirically and enable the empirical optimization of a JITAI intended to increase physical activity (steps/d).

METHODS

We recruited physically inactive English-speaking adults aged ≥25 years who owned smartphones. Participants wore a Fitbit Versa 3 and used the study app for 270 days. The JustWalk JITAI project uses system ID methods to study JIT states. Specifically, provision of support systematically varied across different theoretically plausible operationalizations of JIT states to enable a more rigorous and systematic study of the concept. We experimentally varied 2 intervention components: notifications delivered up to 4 times per day designed to increase a person's steps within the next 3 hours and suggested daily step goals. Notifications to walk were experimentally provided across varied operationalizations of JIT states accounting for need (ie, whether daily step goals were previously met or not), opportunity (ie, whether the next 3 h were a time window during which a person had previously walked), and receptivity (ie, a person previously walked after receiving notifications). Suggested daily step goals varied systematically within a range related to a person's baseline level of steps per day (eg, 4000) until they met clinically meaningful targets (eg, averaging 8000 steps/d as the lower threshold across a cycle). A series of system ID estimation approaches will be used to analyze the data and obtain control-oriented dynamical models to study JIT states. The estimated models from all approaches will be contrasted, with the ultimate goal of guiding rigorous, replicable, empirical formulation and study of JIT states to inform a future JITAI.

RESULTS

As is common in system ID, we conducted a series of simulation studies to formulate the experiment. The results of our simulation studies illustrated the plausibility of this approach for generating informative and unique data for studying JIT states. The study began enrolling participants in June 2022, with a final enrollment of 48 participants. Data collection concluded in April 2023. Upon completion of the analyses, the results of this study are expected to be submitted for publication in the fourth quarter of 2023.

CONCLUSIONS

This study will be the first empirical investigation of JIT states that uses system ID methods to inform the optimization of a scalable JITAI for physical activity.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05273437; https://clinicaltrials.gov/ct2/show/NCT05273437.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52161.

Feasibility of Fitbit Use in Adolescent Survivors of Pediatric Cancer: Who Consistently Uses It and for How Long?

Purpose: Wearable activity trackers with real-time feedback and goal-setting features are being incorporated into programs to increase physical activity among childhood cancer survivors. This analysis describes the adoption and use of a Fitbit® Flex™ among adolescent-aged survivors of childhood cancer without incentives, reminders, or interventions to encourage use. Procedures: Cancer survivors aged 13-18 and ≥2 years from therapy received a Fitbit Flex with instructions to wear it daily. Researchers downloaded participants' daily total steps and active minutes for 1 year. Participants were classified as consistent (≥5 days/week for >4 weeks during the first 12 weeks) or inconsistent users, and comparisons between user types were made. Longitudinal use of the Fitbit and participants' 1-year acceptability evaluations are described. Results: Overall, 67.1% (47/70) of survivors enrolled, and Fitbit data were available for 36 participants. Initially, 30.6% (11/36) were consistent users. Consistent users had lower body mass index z-scores at enrollment (0.4 ± 0.7 vs. 1.2 ± 0.9; p = 0.01), but were otherwise comparable with inconsistent users. Over time survivors' use declined; at 12 months, only one participant was using his or her Fitbit. Survivors who completed a survey (n = 22) reported their Fitbit helped them self-monitor their exercise (72.7%, 16/22) and lead a more active lifestyle (63.6%, 14/22), but found it challenging to charge and not lose, forget about, or break the device. Conclusions: In the real-world setting, only a small subset of adolescent-aged survivors will initially consistently use a Fitbit and their interest diminishes over time. To maximize engagement, interventions incorporating wearable activity trackers likely need to include additional behavior change strategies.

Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children

Importance

The use of consumer-grade wearable devices for collecting data for biomedical research may be associated with social determinants of health (SDoHs) linked to people's understanding of and willingness to join and remain engaged in remote health studies.

Objective

To examine whether demographic and socioeconomic indicators are associated with willingness to join a wearable device study and adherence to wearable data collection in children.

Design, Setting, and Participants

This cohort study used wearable device usage data collected from 10 414 participants (aged 11-13 years) at the year-2 follow-up (2018-2020) of the ongoing Adolescent Brain and Cognitive Development (ABCD) Study, performed at 21 sites across the United States. Data were analyzed from November 2021 to July 2022.

Main Outcomes and Measures

The 2 primary outcomes were (1) participant retention in the wearable device substudy and (2) total device wear time during the 21-day observation period. Associations between the primary end points and sociodemographic and economic indicators were examined.

Results

The mean (SD) age of the 10 414 participants was 12.00 (0.72) years, with 5444 (52.3%) male participants. Overall, 1424 participants (13.7%) were Black; 2048 (19.7%), Hispanic; and 5615 (53.9%) White. Substantial differences were observed between the cohort that participated and shared wearable device data (wearable device cohort [WDC]; 7424 participants [71.3%]) compared with those who did not participate or share data (no wearable device cohort [NWDC]; 2900 participants [28.7%]). Black children were significantly underrepresented (-59%) in the WDC (847 [11.4%]) compared with the NWDC (577 [19.3%]; P < .001). In contrast, White children were overrepresented (+132%) in the WDC (4301 [57.9%]) vs the NWDC (1314 [43.9%]; P < .001). Children from low-income households (<$24 999) were significantly underrepresented in WDC (638 [8.6%]) compared with NWDC (492 [16.5%]; P < .001). Overall, Black children were retained for a substantially shorter duration (16 days; 95% CI, 14-17 days) compared with White children (21 days; 95% CI, 21-21 days; P < .001) in the wearable device substudy. In addition, total device wear time during the observation was notably different between Black vs White children (β = -43.00 hours; 95% CI, -55.11 to -30.88 hours; P < .001).

Conclusions and Relevance

In this cohort study, large-scale wearable device data collected from children showed considerable differences between White and Black children in terms of enrollment and daily wear time. While wearable devices provide an opportunity for real-time, high-frequency contextual monitoring of individuals' health, future studies should account for and address considerable representational bias in wearable data collection associated with demographic and SDoH factors.

Associations between daily step count trajectories and clinical outcomes among adults with comorbid obesity and depression

Purpose

To examine the relationship between features of daily measured step count trajectories and clinical outcomes among people with comorbid obesity and depression in the ENGAGE-2 Trial.

Methods

This post hoc analysis used data from the ENGAGE-2 trial where adults (n=106) with comorbid obesity (BMI ≥30.0 or 27.0 if Asian) and depressive symptoms (Patient Health Questionnaire-9 score ≥10) were randomized (2:1) to receive the experimental intervention or usual care. Daily step count trajectories over the first 60 days (Fitbit Alta HR) were characterized using functional principal component analyses. 7-day and 30-day trajectories were also explored. Functional principal component scores that described features of step count trajectories were entered into linear mixed models to predict weight (kg), depression (Symptom Checklist-20), and anxiety (Generalized Anxiety Disorder Questionnaire-7) at 2-months (2M) and 6-months (6M).

Results

Features of 60-day step count trajectories were interpreted as overall sustained high, continuous decline, and disrupted decline. Overall sustained high step count was associated with low anxiety (2M, β=-0.78, p<.05; 6M, β=-0.80, p<.05) and low depressive symptoms (6M, β=-0.15, p<.05). Continuous decline in step count was associated with high weight (2M, β=0.58, p<.05). Disrupted decline was not associated with clinical outcomes at 2M or 6M. Features of 30-day step count trajectories were also associated with weight (2M, 6M), depression (6M), and anxiety (2M, 6M); Features of 7-day step count trajectories were not associated with weight, depression, or anxiety at 2M or 6M.

Conclusions

Features of step count trajectories identified using functional principal component analysis were associated with depression, anxiety, and weight outcomes among adults with comorbid obesity and depression. Functional principal component analysis may be a useful analytic method that leverages daily measured physical activity levels to allow for precise tailoring of future behavioral interventions.

Feasibility and scalability of a fitness tracker study: Results from a longitudinal analysis of persons with multiple sclerosis

Background

Consumer-grade fitness trackers offer exciting opportunities to study persons with chronic diseases in greater detail and in their daily-life environment. However, attempts to bring fitness tracker measurement campaigns from tightly controlled clinical environments to home settings are often challenged by deteriorating study compliance or by organizational and resource limitations.

Objectives

By revisiting the study design and patient-reported experiences of a partly remote study with fitness trackers (BarKA-MS study), we aimed to qualitatively explore the relationship between overall study compliance and scalability. On that account, we aimed to derive lessons learned on strengths, weaknesses, and technical challenges for the conduct of future studies.

Methods

The two-phased BarKA-MS study employed Fitbit Inspire HR and electronic surveys to monitor physical activity in 45 people with multiple sclerosis in a rehabilitation setting and in their natural surroundings at home for up to 8 weeks. We examined and quantified the recruitment and compliance in terms of questionnaire completion and device wear time. Furthermore, we qualitatively evaluated experiences with devices according to participants' survey-collected reports. Finally, we reviewed the BarKA-MS study conduct characteristics for its scalability according to the Intervention Scalability Assessment Tool checklist.

Results

Weekly electronic surveys completion reached 96%. On average, the Fitbit data revealed 99% and 97% valid wear days at the rehabilitation clinic and in the home setting, respectively. Positive experiences with the device were predominant: only 17% of the feedbacks had a negative connotation, mostly pertaining to perceived measurement inaccuracies. Twenty-five major topics and study characteristics relating to compliance were identified. They broadly fell into the three categories: "effectiveness of support measures", "recruitment and compliance barriers", and "technical challenges". The scalability assessment revealed that the highly individualized support measures, which contributed greatly to the high study compliance, may face substantial scalability challenges due to the strong human involvement and limited potential for standardization.

Conclusion

The personal interactions and highly individualized participant support positively influenced study compliance and retention. But the major human involvement in these support actions will pose scalability challenges due to resource limitations. Study conductors should anticipate this potential compliance-scalability trade-off already in the design phase.

The effect of wearable-delivered sleep interventions on sleep outcomes among adults: A systematic review and meta-analysis of randomized controlled trials

The aims of the review were to (i) evaluate the effectiveness of wearable-delivered sleep interventions on sleep outcomes among adults, and (ii) explore the effect of factors affecting total sleep time. Eight databases were searched to identify relevant studies in English from inception until December 23, 2021. The Cochrane Risk of Bias tool version 2.0 and Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) criteria were used to assess the risk of bias and certainty of the evidence, respectively. Twenty randomized controlled trials (RCTs) were included, involving 1608 adults across nine countries. Wearable-delivered sleep interventions elicited significant improvement of 1.96 events/h for the oxygen desaturation index and 3.13 events/h for the respiratory distress index. Meta-analyses found that wearable-delivered sleep interventions significantly decreased sleep disturbance (Hedges' g [g] = -0.37, 95% confidence interval [CI]: -0.59, -0.15) and sleep-related impairment (g = -1.06, 95% CI: -1.99, -0.13) versus the comparators. The wearable-delivered sleep interventions may complement usual care to improve sleep outcomes. More rigorous RCTs with a long-term assessment in a wide range of populations are warranted.

How is the usability of commercial activity monitors perceived by older adults and by researchers? A cross-sectional evaluation of community-living individuals

OBJECTIVES

Using commercial activity monitors may advance research with older adults. However, usability for the older population is not sufficiently established. This study aims at evaluating the usability of three wrist-worn monitors for older adults. In addition, we report on usability (including data management) for research.

DESIGN

Data were collected cross-sectionally. Between-person of three activity monitor type (Apple Watch 3, Fitbit Charge 4, Polar A370) were made.

SETTING

The activity monitors were worn in normal daily life in an urban community in Germany. The period of wear was 2 weeks.

PARTICIPANTS

Using convenience sampling, we recruited N=27 healthy older adults (≥60 years old) who were not already habitual users of activity monitors.

OUTCOMES

To evaluate usability from the participant perspective, we used the System Usability Scale (SUS) as well as a study-specific qualitative checklist. Assessment further comprised age, highest academic degree, computer proficiency and affinity for technology interaction. Usability from the researchers' perspective was assessed using quantitative data management markers and a study-specific qualitative check-list.

RESULTS

There was no significant difference between monitors in the SUS. Female gender was associated with higher SUS usability ratings. Qualitative participant-usability reports revealed distinctive shortcomings, for example, in terms of battery life and display readability. Usability for researchers came with problems in data management, such as completeness of the data download.

CONCLUSION

The usability of the monitors compared in this work differed qualitatively. Yet, the overall usability ratings by participants were comparable. Conversely, from the researchers' perspective, there were crucial differences in data management and usability that should be considered when making monitor choices for future studies.

The Collaborative Lifestyle Intervention Program in Knee Osteoarthritis Patients (CLIP-OA) trial: Design and methods

Being overweight or obese is a primary modifiable risk factor that exacerbates disease progression and mobility disability in older knee osteoarthritis (OA) patients. Lifestyle interventions combining exercise with dietary weight loss (EX+DWL) yield meaningful improvements in mobility and weight loss that are superior to EX or DWL alone. Unfortunately, community access to practical, sustainable weight management interventions remains limited and places knee OA patients at increased risk of mobility disability. The Collaborative Lifestyle Intervention Program in Knee Osteoarthritis patients (CLIP-OA), was a two-arm, 18 month randomized-controlled, comparative effectiveness trial designed to contrast the effects of an evidence-based, theory-driven EX+DWL intervention, personalized to patient needs and delivered by our community partners, with those of the Arthritis Foundation's Walk With Ease (WWE) standard of care self-management program in the treatment of knee OA patients with overweight or obesity. The primary outcome of the CLIP-OA trial was mobility performance assessed using the 400-m walk test (400MWT). Secondary outcomes included weight loss, pain, select quality of life and social cognitive variables, and cost-effectiveness of intervention delivery. Findings from the CLIP-OA trial will determine the comparative and cost-effectiveness of the EX+DWL and WWE interventions on key clinical outcomes and has the potential to offer a sustainable medium for intervention delivery that can promote widely accessible weight management among knee OA patients with overweight or obesity. Trial Registration: NCT02835326.

The Effect of Tailored, Daily Smartphone Feedback to Lifestyle Self-Monitoring on Weight Loss at 12 Months: The SMARTER Randomized Clinical Trial (Preprint)

Background

Self-monitoring (SM) is the centerpiece of behavioral weight loss treatment, but the efficacy of smartphone-delivered SM feedback (FB) has not been tested in large, long-term, randomized trials.

Objective

The aim of this study was to establish the efficacy of providing remote FB to diet, physical activity (PA), and weight SM on improving weight loss outcomes when comparing the SM plus FB (SM+FB) condition to the SM-only condition in a 12-month randomized controlled trial. The study was a single-site, population-based trial that took place in southwestern Pennsylvania, USA, conducted between 2018 and 2021. Participants were smartphone users age ≥18 years, able to engage in moderate PA, with a mean BMI between 27 and 43 kg/m².

Methods

All participants received a 90-minute, one-to-one, in-person behavioral weight loss counseling session addressing behavioral strategies, establishing participants’ dietary and PA goals, and instructing on use of the PA tracker (Fitbit Charge 2), smart scale, and diet SM app. Only SM+FB participants had access to an investigator-developed smartphone app that read SM data, in which an algorithm selected tailored messages sent to the smartphone up to 3 times daily. The SM-only participants did not receive any tailored FB based on SM data. The primary outcome was percent weight change from baseline to 12 months. Secondary outcomes included engagement with digital tools (eg, monthly percentage of FB messages opened and monthly percentage of days adherent to the calorie goal).

Results

Participants (N=502) were on average 45.0 (SD 14.4) years old with a mean BMI of 33.7 (SD 4.0) kg/m². The sample was 79.5% female (n=399/502) and 82.5% White (n=414/502). At 12 months, retention was 78.5% (n=394/502) and similar by group (SM+FB: 202/251, 80.5%; SM: 192/251, 76.5%; P=.28). There was significant percent weight loss from baseline in both groups (SM+FB: –2.12%, 95% CI –3.04% to –1.21%, P<.001; SM: –2.39%, 95% CI –3.32% to –1.47%; P<.001), but no difference between the groups (–0.27%; 95% CI –1.57% to 1.03%; t =–0.41; P=.68). Similarly, 26.3% (66/251) of the SM+FB group and 29.1% (73/251) of the SM group achieved ≥5% weight loss (chi-square value=0.49; P=.49). A 1% increase in FB messages opened was associated with a 0.10 greater percent weight loss at 12 months (b=–0.10; 95% CI –0.13 to –0.07; t =–5.90; P<.001). A 1% increase in FB messages opened was associated with 0.12 greater percentage of days adherent to the calorie goal per month (b=0.12; 95% CI 0.07-0.17; F=22.19; P<.001).

Conclusions

There were no significant between-group differences in weight loss; however, the findings suggested that the use of commercially available digital SM tools with or without FB resulted in a clinically significant weight loss in over 25% of participants. Future studies need to test additional strategies that will promote greater engagement with digital tools.

Trial Registration

Clinicaltrials.gov NCT03367936; https://clinicaltrials.gov/ct2/show/NCT03367936

A Remote Health Coaching, Text-Based Walking Program in Ethnic Minority Primary Care Patients With Overweight and Obesity: Feasibility and Acceptability Pilot Study

Background

Over half of US adults have at least one chronic disease, including obesity. Although physical activity is an important component of chronic disease self-management, few reach the recommended physical activity goals. Individuals who identify as racial and ethnic minorities are disproportionally affected by chronic diseases and physical inactivity. Interventions using consumer-based wearable devices have shown promise for increasing physical activity among patients with chronic diseases; however, populations with the most to gain, such as minorities, have been poorly represented to date.

Objective

This study aims to assess the feasibility, acceptability, and preliminary outcomes of an 8-week text-based coaching and Fitbit program aimed at increasing the number of steps in a predominantly overweight ethnic minority population.

Methods

Overweight patients (BMI >25 kg/m²) were recruited from an internal medicine clinic located in an inner-city academic medical center. Fitbit devices were provided. Using 2-way SMS text messaging, health coaches (HCs) guided patients to establish weekly step goals that were specific, measurable, attainable, realistic, and time-bound. SMS text messaging and Fitbit activities were managed using a custom-designed app. Program feasibility was assessed via the recruitment rate, retention rate (the proportion of eligible participants completing the 8-week program), and patient engagement (based on the number of weekly text message goals set with the HC across the 8-week period). Acceptability was assessed using a qualitative, summative evaluation. Exploratory statistical analysis included evaluating the average weekly steps in week 1 compared with week 8 using a paired t test (2-tailed) and modeling daily steps over time using a linear mixed model.

Results

Of the 33 patients initially screened; 30 (91%) patients were enrolled in the study. At baseline, the average BMI was 39.3 (SD 9.3) kg/m², with 70% (23/33) of participants presenting as obese. A total of 30% (9/30) of participants self-rated their health as either fair or poor, and 73% (22/30) of participants set up ≥6 weekly goals across the 8-week program. In total, 93% (28/30) of participants completed a qualitative summative evaluation, and 10 themes emerged from the evaluation: patient motivation, convenient SMS text messaging experience, social support, supportive accountability, technology support, self-determined goals, achievable goals, feedback from Fitbit, challenges, and habit formation. There was no significant group change in the average weekly steps for week 1 compared with week 8 (mean difference 7.26, SD 6209.3; P=.99). However, 17% (5/30) of participants showed a significant increase in their daily steps.

Conclusions

Overall, the results demonstrate the feasibility and acceptability of a remotely delivered walking study that included an HC; SMS text messaging; a wearable device (Fitbit); and specific, measurable, attainable, realistic, and time-bound goals within an ethnic minority patient population. Results support further development and testing in larger samples to explore efficacy.

Smartphone Ownership and Interest in Mobile Health Technologies for Self-care Among Patients With Chronic Heart Failure: Cross-sectional Survey Study

Background

Heart failure (HF) is a highly prevalent chronic condition that places a substantial burden on patients, families, and health care systems worldwide. Recent advances in mobile health (mHealth) technologies offer great opportunities for supporting many aspects of HF self-care. There is a need to better understand patients’ adoption of and interest in using mHealth for self-monitoring and management of HF symptoms.

Objective

The purpose of this study is to assess smartphone ownership and patient attitudes toward using mHealth technologies for HF self-care in a predominantly minority population in an urban clinical setting.

Methods

We conducted a cross-sectional survey of adult outpatients (aged ≥18 years) at an academic outpatient HF clinic in the Midwest. The survey comprised 34 questions assessing patient demographics, ownership of smartphones and other mHealth devices, frequently used smartphone features, use of mHealth apps, and interest in using mHealth technologies for vital sign and HF symptom self-monitoring and management.

Results

A total of 144 patients were approached, of which 100 (69.4%) participated in the study (63/100, 63% women). The participants had a mean age of 61.3 (SD 12.25) years and were predominantly Black or African American (61/100, 61%) and Hispanic or Latino (18/100, 18%). Almost all participants (93/100, 93%) owned a cell phone. The share of patients who owned a smartphone was 68% (68/100). Racial and ethnic minorities that identified as Black or African American or Hispanic or Latino reported higher smartphone ownership rates compared with White patients with HF (45/61, 74% Black or African American and 11/18, 61% Hispanic or Latino vs 9/17, 53% White). There was a moderate and statistically significant association between smartphone ownership and age (Cramér V [Φ_C]=0.35; P<.001), education (Φ_C=0.29; P=.001), and employment status (Φ_C=0.3; P=.01). The most common smartphone features used by the participants were SMS text messaging (51/68, 75%), internet browsing (43/68, 63%), and mobile apps (41/68, 60%). The use of mHealth apps and wearable activity trackers (eg, Fitbits) for self-monitoring of HF-related parameters was low (15/68, 22% and 15/100, 15%, respectively). The most popular HF-related self-care measures participants would like to monitor using mHealth technologies were physical activity (46/68, 68%), blood pressure (44/68, 65%), and medication use (40/68, 59%).

Conclusions

Most patients with HF have smartphones and are interested in using commercial mHealth apps and connected health devices to self-monitor their condition. Thus, there is a great opportunity to capitalize on the high smartphone ownership among racial and ethnic minority patients to increase reach and enhance HF self-management through mHealth interventions.

Consumer Willingness to Share Personal Digital Information for Health-Related Uses

IMPORTANCE

Consumers routinely generate digital information that reflects on their health.

OBJECTIVE

To evaluate the factors associated with consumers' willingness to share their digital health information for research, health care, and commercial uses.

DESIGN, SETTING, AND PARTICIPANTS

This national survey with an embedded conjoint experiment recruited US adults from a nationally representative sample, with oversampling of Black and Hispanic panel members. Participants were randomized to 15 scenarios reflecting use cases for consumer digital information from a total of 324 scenarios. Attributes of the conjoint analysis included 3 uses, 3 users, 9 sources of digital information, and 4 relevant health conditions. The survey was conducted from July 10 to 31, 2020.

MAIN OUTCOMES AND MEASURES

Participants rated each conjoint profile on a 5-point Likert scale (1-5) measuring their willingness to share their personal digital information (with 5 indicating the most willingness to share). Results reflect mean differences in this scale from a multivariable regression model.

RESULTS

Among 6284 potential participants, 3543 (56%) responded. A total of 1862 participants (53%) were female, 759 (21%) identified as Black, 834 (24%) identified as Hispanic, and 1274 (36%) were 60 years or older. In comparison with information from electronic health care records, participants were less willing to share information about their finances (coefficient, -0.56; 95% CI, -0.62 to -0.50), places they visit from public cameras (coefficient, -0.28; 95% CI, -0.33 to -0.22), communication on social media (coefficient, -0.20; 95% CI -0.26 to -0.15), and their search history from internet search engines (coefficient, -0.11; 95% CI, -0.17 to -0.06). They were more willing to share information about their steps from applications on their phone (coefficient, 0.22; 95% CI, 0.17-0.28). Among the conjoint attributes, the source of information (importance weight: 59.1%) was more important than the user (17.3%), use (12.3%), and health condition (11.3%). Four clusters of consumers emerged from the sample with divergent privacy views. While the context of use was important, these 4 groups expressed differences in their overall willingness to share, with 337 participants classified as never share; 1116 classified as averse to sharing (mean rating, 1.64; 95% CI, 1.62-1.65); 1616 classified as uncertain about sharing (mean rating, 2.84; 95% CI, 2.81-2.86); and 474 classified as agreeable to sharing (mean rating, 4.18; 95% CI, 4.16-4.21). Respondents who identified as White and non-Hispanic, had higher income, and were politically conservative were more likely to be in a cluster that was less willing to share (ie, never or averse clusters).

CONCLUSIONS AND RELEVANCE

These findings suggest that although consumers' willingness to share personal digital information for health purposes is associated with the context of use, many have strong underlying privacy views that affect their willingness to share. New protections may be needed to give consumers confidence to be comfortable sharing their personal information.

Validity of a Global Positioning System-Based Algorithm and Consumer Wearables for Classifying Active Trips in Children and Adults

Objective

To investigate the convergent validity of a global positioning system (GPS)-based and two consumer-based measures with trip logs for classifying pedestrian, cycling, and vehicle trips in children and adults.

Methods

Participants (N = 34) wore a Qstarz GPS tracker, Fitbit Alta, and Garmin vivosmart 3 on multiple days and logged their outdoor pedestrian, cycling, and vehicle trips. Logged trips were compared with device-measured trips using the Personal Activity Location Measurement System (PALMS) GPS-based algorithms, Fitbit's SmartTrack, and Garmin's Move IQ. Trip- and day-level agreement were tested.

Results

The PALMS identified and correctly classified the mode of 75.6%, 94.5%, and 96.9% of pedestrian, cycling, and vehicle trips (84.5% of active trips, F1 = 0.84 and 0.87) as compared with the log. Fitbit and Garmin identified and correctly classified the mode of 26.8% and 17.8% (22.6% of active trips, F1 = 0.40 and 0.30) and 46.3% and 43.8% (45.2% of active trips, F1 = 0.58 and 0.59) of pedestrian and cycling trips. Garmin was more prone to false positives (false trips not logged). Day-level agreement for PALMS and Garmin versus logs was favorable across trip modes, though PALMS performed best. Fitbit significantly underestimated daily cycling. Results were similar but slightly less favorable for children than adults.

Conclusions

The PALMS showed good convergent validity in children and adults and were about 50% and 27% more accurate than Fitbit and Garmin (based on F1). Empirically-based recommendations for improving PALMS' pedestrian classification are provided. Since the consumer devices capture both indoor and outdoor walking/running and cycling, they are less appropriate for trip-based research.

Wearable activity trackers and artificial intelligence in the management of rheumatic diseases : Where are we in 2021?

Wearable activity trackers are playing an increasingly important role in healthcare. In the field of rheumatic and musculoskeletal diseases (RMDs), various applications are currently possible. This review will present the use of activity trackers to promote physical activity levels in rheumatology, as well as the use of trackers to measure health parameters and detect flares using artificial intelligence. Challenges and limitations of the use of artificial intelligence will be discussed, as well as technical issues when using activity trackers in clinical practice.

Use of Fitbit Devices in Physical Activity Intervention Studies Across the Life Course: Narrative Review

BACKGROUND

Commercial off-the-shelf activity trackers (eg, Fitbit) allow users to self-monitor their daily physical activity (PA), including the number of steps, type of PA, amount of sleep, and other features. Fitbits have been used as both measurement and intervention tools. However, it is not clear how they are being incorporated into PA intervention studies, and their use in specific age groups across the life course is not well understood.

OBJECTIVE

This narrative review aims to characterize how PA intervention studies across the life course use Fitbit devices by synthesizing and summarizing information on device selection, intended use (intervention vs measurement tool), participant wear instructions, rates of adherence to device wear, strategies used to boost adherence, and the complementary use of other PA measures. This review provides intervention scientists with a synthesis of information that may inform future trials involving Fitbit devices.

METHODS

We conducted a search of the Fitabase Fitbit Research Library, a database of studies published between 2012 and 2018. Of the 682 studies available on the Fitabase research library, 60 interventions met the eligibility criteria and were included in this review. A supplemental search in PubMed resulted in the inclusion of 15 additional articles published between 2019 and 2020. A total of 75 articles were reviewed, which represented interventions conducted in childhood; adolescence; and early, middle, and older adulthood.

RESULTS

There was considerable heterogeneity in the use of Fitbit within and between developmental stages. Interventions for adults typically required longer wear periods, whereas studies on children and adolescents tended to have more limited device wear periods. Most studies used developmentally appropriate behavior change techniques and device wear instructions. Regardless of the developmental stage and intended Fitbit use (ie, measurement vs intervention tool), the most common strategies used to enhance wear time included sending participants reminders through texts or emails and asking participants to log their steps or synchronize their Fitbit data daily. The rates of adherence to the wear time criteria were reported using varying metrics. Most studies supplemented the use of Fitbit with additional objective or self-reported measures for PA.

CONCLUSIONS

Overall, the heterogeneity in Fitbit use across PA intervention studies reflects its relative novelty in the field of research. As the use of monitoring devices continues to expand in PA research, the lack of uniformity in study protocols and metrics of reported measures represents a major issue for comparability purposes. There is a need for increased transparency in the prospective registration of PA intervention studies. Researchers need to provide a clear rationale for the use of several PA measures and specify the source of their main PA outcome and how additional measures will be used in the context of Fitbit-based interventions.