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.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is a common, complex syndrome associated with elevated morbidity and mortality. Patients with HFpEF have a high prevalence of comorbidities, including hypertension, diabetes mellitus, and obesity, which are closely related to the underlying mechanisms of the disease. Lifestyle modification with weight loss and physical activity can improve risk factors and functional outcomes in HFpEF. We sought to observe daily physical activity and determine whether utilizing an activity tracker can enhance functional status in HFpEF patients.
METHODS: We performed a prospective analysis of 57 patients with HFpEF from 2021 to 2023 at a single academic medical center who utilized a Fitbit to record one year of daily step activity. The patients were evaluated in the ambulatory setting for an initial visit and subsequently at intervals of 3, 6, and 12 months to gather vitals, labs, physical exam, and functional measurements, including the Six-Minute Walk Test (6MWT) and Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12). Associations between variables were assessed using Pearson\'s r correlation using Stata 18.0.
RESULTS:  Of the 49 patients who completed the study, the mean age was 68.1 ± 10.2 years, with 67% of patients identifying as female. The average BMI was 36.4 ± 8.6 kg/m2. Across each time interval, the median numbers of steps per day were 4,113 (2,517-6,520) (1-3 months), 4,583 (2,532-6,326) (4-6 months), and 3,957 (2,942-5,982) (7-12 months). There was no statistically significant variation in daily step count (p=0.06). We observed a statistically significant increase of 66 (6-200) feet in the 6MWT (p= 0.002) from baseline (1,175 (910-1,400)) to 12 months (1,321 (1,000-1,550)). The daily step count was highly correlated with the 6MWT across all time points (1-3 months: r= .70, p< .001; 4-6 months: r= .61, p< .001; 7-12 months: r= .69, p< .001). The total KCCQ-12 scores increased by 6.8 (-4.2-19.8) points (p=0.005) from baseline (60.1 (41.7-73.4)) to 12 months (69.8 (50-84.4)). Among the sub-categories of the questionnaire, we observed a positive correlation between physical limitation scores and daily step count (1-3 months: r= .47, p=.001; 4-6 months: r= .63, p< .001; 7-12 months: r= .56, p= .001). Of interest, one patient who was taking over 15,000 daily steps scored their physical limitation 10-20 points lower than those taking less than half the steps and had one of the lowest quality of life scores in the cohort, reflecting the subjective nature of heart failure (HF) symptoms.
CONCLUSIONS: Fitbit technology offers a convenient means to monitor real-time physical activity in patients with HFpEF. Utilizing a Fitbit to record daily step activity enhances health-related quality of life in this population. In contrast to the improved average total KCCQ-12 score, we did not observe a clinically significant increase in the 6MWT over the course of the year. Our findings establish the utility of daily step count as a valuable surrogate for six-minute walk distance.

Electronic health (eHealth) and mobile health (mHealth) could stimulate physical activity (PA) in a time-efficient and cost-effective way. This randomized controlled trial aims to investigate effects on moderate-to-vigorous PA (MVPA) of different combined computer- and mobile-based PA interventions targeted at adults aged 50 years and over. Participants (N = 954) were randomly allocated to a basic existing computer-based intervention (Active Plus [AP] or I Move [IM]) supplemented with one of three mobile elements being (1) activity tracker (AT), (2) ecological momentary intervention (EMI), or (3) chatbot (CB) or a control group (CG). MVPA was assessed via the SQUASH at baseline (T0), 3 months (T1), and 6 months (T2) and via accelerometers at T0 and T2. No intervention effects were found on objective (p = .502) and subjective (p = .368) MVPA for main research groups (AP/IM + AT, AP/IM + EMI, AP/IM + CB). Preliminary MVPA findings for subgroups (AP + AT, AP + EMI, AP + CB, IM + AT, IM + EMI, IM + CB) combined with drop-out data showed potential for the computer-based intervention AP with an integrated AT. Based on these preliminary findings, eHealth developers can be recommended to integrate ATs with existing computer-based PA interventions. However, further research is recommended to confirm the findings as a result of the exploratory nature of the subgroup analyses.

BACKGROUND: The self-monitoring of physical activity is an effective strategy for promoting active lifestyles. However, accurately assessing physical activity remains challenging in certain situations. This study evaluates a novel floor-vibration monitoring system to quantify housework-related physical activity.
OBJECTIVE: This study aims to assess the validity of step-count and physical behavior intensity predictions of a novel floor-vibration monitoring system in comparison with the actual number of steps and indirect calorimetry measurements. The accuracy of the predictions is also compared with that of research-grade devices (ActiGraph GT9X).
METHODS: The Ocha-House, located in Tokyo, serves as an independent experimental facility equipped with high-sensitivity accelerometers installed on the floor to monitor vibrations. Dedicated data processing software was developed to analyze floor-vibration signals and calculate 3 quantitative indices: floor-vibration quantity, step count, and moving distance. In total, 10 participants performed 4 different housework-related activities, wearing ActiGraph GT9X monitors on both the waist and wrist for 6 minutes each. Concurrently, floor-vibration data were collected, and the energy expenditure was measured using the Douglas bag method to determine the actual intensity of activities.
RESULTS: Significant correlations (P<.001) were found between the quantity of floor vibrations, the estimated step count, the estimated moving distance, and the actual activity intensities. The step-count parameter extracted from the floor-vibration signal emerged as the most robust predictor (r2=0.82; P<.001). Multiple regression models incorporating several floor-vibration-extracted parameters showed a strong association with actual activity intensities (r2=0.88; P<.001). Both the step-count and intensity predictions made by the floor-vibration monitoring system exhibited greater accuracy than those of the ActiGraph monitor.
CONCLUSIONS: Floor-vibration monitoring systems seem able to produce valid quantitative assessments of physical activity for selected housework-related activities. In the future, connected smart home systems that integrate this type of technology could be used to perform continuous and accurate evaluations of physical behaviors throughout the day.

Osteoarthritis (OA) poses a growing challenge for the aging population, especially in the hip and knee joints, contributing significantly to disability and societal costs. Exploring the integration of wearable technology, this study addresses the limitations of traditional rehabilitation assessments in capturing real-world experiences and dynamic variations. Specifically, it focuses on continuously monitoring physical activity in hip and knee OA patients using automated unsupervised evaluations within the rehabilitation process. We analyzed data from 1144 patients who used a mobile health application after surgery; the activity data were collected using the Garmin Vivofit 4. Several parameters, such as the total number of steps per day, the peak 6-minute consecutive cadence (P6MC) and peak 1-minute cadence (P1M), were computed and analyzed on a daily basis. The results indicated that cadence-based measurements can effectively, and earlier, differ among patients with hip and knee conditions, as well as in the recovery process. Comparisons based on recovery status and type of surgery reveal distinctive trajectories, emphasizing the effectiveness of P6MC and P1M in detecting variations earlier than total steps per day. Furthermore, cadence-based measurements showed a lower inter-day variability (40%) compared to the total number of steps per day (80%). Automated assessments, including P1M and P6MC, offer nuanced insights into the patients\' dynamic activity profiles.

BACKGROUND: This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants.
OBJECTIVE: The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL.
METHODS: A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement.
RESULTS: Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=-0.3% to 0.9%), 1.19% for standing (LoA=-1.5% to 3.42%), and -4.71% for walking (LoA=-9.26% to -0.16%). The mean biases between SORD and activPAL were -3.45% for sitting and reclining (LoA=-11.59% to 4.68%), 7.45% for standing (LoA=-5.04% to 19.95%), and -5.40% for walking (LoA=-11.44% to 0.64%).
CONCLUSIONS: Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior.

UNASSIGNED: Understanding the experiences of people with Chronic Obstructive Pulmonary Disease (COPD) using activity monitors in daily life could support the utilisation of technology within healthcare to increase physical activity and support self-management. This qualitative study aimed to explore the experiences of people with COPD using activity monitors at home in everyday life.
UNASSIGNED: Semi-structured face-to-face or telephone interviews were conducted with seven people with COPD between August 2018 and June 2020. Participants had all used an activity monitor within the last year (Fitbit, Garmin, or Apple Watch). Interviews were analysed in-depth using Interpretative Phenomenological Analysis (IPA).
UNASSIGNED: Four themes, developed using IPA, highlight participants\' engagement with activity monitors and integrating them into their lives: (1) Motivational features to monitor activity, (2) Importance of setting achievable goals, (3) Developing knowledge and awareness, and (4) Integration into everyday life for self-management.
UNASSIGNED: Activity monitors were perceived to be beneficial and useful to people with COPD, not just for monitoring their activity, but also helping to self-manage their condition. Activity monitors may be a useful tool within rehabilitation and healthcare services for COPD.
Activity monitors were beneficial for people with Chronic Obstructive Pulmonary Disease (COPD) to monitor their physical activity and support self-management of their COPD.People with COPD could see and make sense of their activity levels, set activity goals and increase their motivation from the objectively monitored activity.Activity monitors can help to support individual goal setting and facilitate ownership, but support is needed to set achievable and realistic goals.Healthcare practitioners need to be aware of the potential negative effects of using activity monitors on mental or physical health and wellbeing and support people with COPD to manage pressure and expectations of meeting their goals.

BACKGROUND: Physical activity (PA) and cardiac rehabilitation (CR) attendance are important for recovery and prognosis following acute coronary syndrome (ACS). However, PA patterns early post-ACS are not well known.
OBJECTIVE: Investigate the level of PA at 4-weeks post-ACS and any potential associations with CR enrolment.
METHODS: We recruited patients admitted for ACS from cardiac wards and clinics at two hospital sites in Sydney, Australia. PA data were collected using wearable activity trackers worn at 4-weeks post-ACS, and CR enrolment was self-reported.
RESULTS: Participants (n = 61) were aged 66.7 ± 10.3 years, 74 % male, 61 % were married or partnered, and 33 % were diagnosed with ST-elevation myocardial infarction. Patients engaged in 7514±3355 steps per day and 44.6 ± 37.5 min of moderate-to-vigorous physical activity (MVPA). Patients who enrolled in CR exhibited higher daily step counts (p = 0.044), MVPA minutes (p = 0.001), and were more likely to meet PA guidelines. ACS patients who engaged in higher levels of MVPA were more likely to enrol in CR (odds ratio [OR] 1.46; 95 % confidence interval [CI] 1.08, 1.98). CR enrolment was also positively associated with being married or in an intimate partnership (OR 9.93; 95 % CI 1.83, 53.85) and absence of depressive symptoms (OR 11.86; 95 % CI 1.91, 73.74).
CONCLUSIONS: Lower CR enrolment rates were observed among less physically active patients at 4-weeks post-ACS. However, each 10 min increment in MVPA increased the odds of CR enrolment by 46 %. Future research should explore strategies to target this inactive and high-risk group, given the potential for a large prognostic gain with CR participation.

BACKGROUND: Insufficient physical activity is a public health concern. New technologies may improve physical activity levels and enable the identification of its predictors with high accuracy. The Precious smartphone app was developed to investigate the effect of specific modular intervention elements on physical activity and examine theory-based predictors within individuals.
OBJECTIVE: This study pilot-tested a fully automated factorial N-of-1 randomized controlled trial (RCT) with the Precious app and examined whether digitalized motivational interviewing (dMI) and heart rate variability-based biofeedback features increased objectively recorded steps. The secondary aim was to assess whether daily self-efficacy and motivation predicted within-person variability in daily steps.
METHODS: In total, 15 adults recruited from newspaper advertisements participated in a 40-day factorial N-of-1 RCT. They installed 2 study apps on their phones: one to receive intervention elements and one to collect ecological momentary assessment (EMA) data on self-efficacy, motivation, perceived barriers, pain, and illness. Steps were tracked using Xiaomi Mi Band activity bracelets. The factorial design included seven 2-day biofeedback interventions with a Firstbeat Bodyguard 2 (Firstbeat Technologies Ltd) heart rate variability sensor, seven 2-day dMI interventions, a wash-out day after each intervention, and 11 control days. EMA questions were sent twice per day. The effects of self-efficacy, motivation, and the interventions on subsequent steps were analyzed using within-person dynamic regression models and aggregated data using longitudinal multilevel modeling (level 1: daily observations; level 2: participants). The analyses were adjusted for covariates (ie, within- and between-person perceived barriers, pain or illness, time trends, and recurring events).
RESULTS: All participants completed the study, and adherence to activity bracelets and EMA measurements was high. The implementation of the factorial design was successful, with the dMI features used, on average, 5.1 (SD 1.0) times of the 7 available interventions. Biofeedback interventions were used, on average, 5.7 (SD 1.4) times out of 7, although 3 participants used this feature a day later than suggested and 1 did not use it at all. Neither within- nor between-person analyses revealed significant intervention effects on step counts. Self-efficacy predicted steps in 27% (4/15) of the participants. Motivation predicted steps in 20% (3/15) of the participants. Aggregated data showed significant group-level effects of day-level self-efficacy (B=0.462; P<.001), motivation (B=0.390; P<.001), and pain or illness (B=-1524; P<.001) on daily steps.
CONCLUSIONS: The automated factorial N-of-1 trial with the Precious app was mostly feasible and acceptable, especially the automated delivery of the dMI components, whereas self-conducted biofeedback measurements were more difficult to time correctly. The findings suggest that changes in self-efficacy and motivation may have same-day effects on physical activity, but the effects vary across individuals. This study provides recommendations based on the lessons learned on the implementation of factorial N-of-1 RCTs.

Increased physical activity has been demonstrated as a relevant treatment after a stroke, with positive effects on impairment recovery, activity limitation, social participation and quality of life. Furthermore, PA is now recommended as part of the stroke recovery pathway, starting during inpatient care and extending through rehabilitation and community integration. The purpose of this presentation is to describe how current technologies may facilitate a continuity of care for stroke survivors. We present a synthesis of 8 studies that we have conducted to date to assess and monitor the activity level of post-stroke patients at home. The results of these studies show that home rehabilitation of post-stroke patients requires the use of individualized monitoring criteria to optimize patient care. To encourage the patient to increase his level of moderate physical activity and reduce his sedentary time, it would be recommended to propose a regularly monitored and structured program.