Low validity of the Sensewear Pro3 activity monitor compared to indirect calorimetry during simulated free living in patients with osteoarthritis of the hip

BACKGROUND: To validate physical activity estimates by the Sensewear Pro3 activity monitor compared with indirect calorimetry during simulated free living in patients diagnosed with osteoarthritis of the hip pre or post total hip arthroplasty. METHODS: Twenty patients diagnosed with hip osteoarthritis (10 pre- and 10 post total hip arthroplasty; 40% female; age: 63.3 ± 9.0; BMI: 23.7 ± 3.7). All patients completed a 2 hour protocol of simulated free living with 8 different typical physical activity types. Energy consumption (kcal/min) was estimated by the Sense Wear pro3 Armband activity monitor and validated against indirect calorimetry (criterion method) by means of a portable unit (Cosmed K4b(2)). Bias and variance was analyzed using functional ANOVA. RESULTS: Mean bias during all activities was 1.5 Kcal/min 95%CI [1.3; 1.8] corresponding to 72% (overestimation). Normal gait speed showed an overestimation of 2.8 Kcal/min, 95%CI [2.3; 3.3] (93%) while an underestimation of -1.1 Kcal/min, 95%CI [-1.8; -0.3] (-25%) was recorded during stair climb. Activities dominated by upper body movements showed large overestimation with 4.37 Kcal/min, 95%CI [3.8; 5.1] (170%) being recorded during gardening. Both bias and variance appeared to be dependent on activity type. CONCLUSION: The activity monitor generally overestimated the energy consumption during common activities of low to medium intensity in the patient group. The size and direction of the bias was highly dependent on the activity type which indicates the activity monitor is of limited value in patients with hip osteoarthritis and that the results do not express the real energy expenditure

Mechanical and free living comparisons of four generations of the Actigraph activity monitor.

BACKGROUND: More studies include multiple generations of the Actigraph activity monitor. So far no studies have compared the output including the newest generation and investigated the impact on the output of the activity monitor when enabling the low frequency extension (LFE) option. The aims were to study the responses of four generations (AM7164, GT1M, GT3X and GT3X+) of the Actigraph activity monitor in a mechanical setup and a free living environment with and without enabling the LFE option. METHODS: The monitors were oscillated in a mechanical setup using two radii in the frequency range 0.25-3.0 Hz. Following the mechanical study a convenience sample (N = 20) wore three monitors (one AM7164 and two GT3X) for 24 hours. RESULTS: The AM7164 differed from the newer generations across frequencies (p  0.05 for differences between generations) thus attenuated the difference in mean PA (p > 0.05) when the LFE option was enabled. However, it did not attenuate the difference in time spend in vigorous PA and it introduced a difference in time spend in moderate PA (+ 3.0 min (95% CI 0.4 to 5.6)) between the generations. CONCLUSION: We observed significant differences between the AM7164 and the newer Actigraph GT-generations (GT1M, GT3X and GT3X+) in a mechanical setup and in free-living. Enabling the LFE option attenuated the differences in mean PA completely, but induced a bias in the moderate PA intensities.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Effect of ecological momentary assessment, goal-setting and personalized phone-calls on adherence to interval walking training using the InterWalk application among patients with type 2 diabetes-A pilot randomized controlled trial

<div><p>Objectives</p><p>The objective was to investigate the feasibility and usability of electronic momentary assessment, goal-setting and personalized phone-calls on adherence to a 12-week self-conducted interval walking training (IWT) program, delivered by the InterWalk smartphone among patients with type 2 diabetes (T2D).</p><p>Methods</p><p>In a two-arm pilot randomized controlled trial (Denmark, March 2014 to February 2015), patients with T2D (18–80 years with a Body Mass Index of 18 and 40 kg/m2) were randomly allocated to 12 weeks of IWT with (experimental) or without additional support (control). The primary outcome was the difference between groups in accumulated time of interval walking training across 12 weeks. All patients were encouraged to use the InterWalk application to perform IWT for ≥90 minute/week. Patients in the experimental group made individual goals regarding lifestyle change. Once a week inquiries about exercise adherence was made using an ecological momentary assessment (EMA). In case of consistent self-reported non-adherence, the patients would receive a phone-call inquiring about the reason for non-adherence. The control group did not receive additional support. Information about training adherence was assessed objectively. Usability of the EMA was assessed based on response rates and self-reported satisfaction after 12-weeks.</p><p>Results</p><p>Thirty-seven patients with T2D (66 years, 65% female, hemoglobin 1Ac 50.3 mmol/mol) where included (n = 18 and n = 19 in experimental and control group, respectively). The retention rate was 83%. The experimental group accumulated [95%CI] 345 [-7, 698] minutes of IWT more than the control group. The response rate for the text-messages was 83% (68% for males and 90% for females). Forty-one percent of the experimental and 25% of the control group were very satisfied with their participation.</p><p>Conclusion</p><p>The combination inquiry about adherence using EMA, goal-setting with the possibility of follow-up phone calls are considered feasible interventions to attain training adherence when using the InterWalk app during a 12-week period in patients with T2D. Some uncertainty about the effect size of adherence remains.</p><p>Trial registration</p><p>Clinicaltrials.gov <a href="https://clinicaltrials.gov/ct2/show/NCT02089477" target="_blank">NCT02089477</a></p></div

Development of the Multidimensional Readiness and Enablement Index for Health Technology (READHY) Tool to Measure Individuals' Health Technology Readiness:Initial Testing in a Cancer Rehabilitation Setting

BACKGROUND: The increasing digitization of health care services with enhanced access to fast internet connections, along with wide use of smartphones, offers the opportunity to get health advice or treatment remotely. For service providers, it is important to consider how consumers can take full advantage of available services and how this can create an enabling environment. However, it is important to consider the digital context and the attributes of current and future users, such as their readiness (ie, knowledge, skills, and attitudes, including trust and motivation). OBJECTIVE: The objective of this study was to evaluate how the eHealth Literacy Questionnaire (eHLQ) combined with selected dimensions from the Health Education Impact Questionnaire (heiQ) and the Health Literacy Questionnaire (HLQ) can be used together as an instrument to characterize an individual\u27s level of health technology readiness and explore how the generated data can be used to create health technology readiness profiles of potential users of health technologies and digital health services. METHODS: We administered the instrument and sociodemographic questions to a population of 305 patients with a recent cancer diagnosis referred to rehabilitation in a setting that plans to introduce various technologies to assist the individuals. We evaluated properties of the Readiness and Enablement Index for Health Technology (READHY) instrument using confirmatory factor analysis, convergent and discriminant validity analysis, and exploratory factor analysis. To identify different health technology readiness profiles in the population, we further analyzed the data using hierarchical and k-means cluster analysis. RESULTS: The confirmatory factor analysis found a suitable fit for the 13 factors with only 1 cross-loading of 1 item between 2 dimensions. The convergent and discriminant validity analysis revealed many factor correlations, suggesting that, in this population, a more parsimonious model might be achieved. Exploratory factor analysis pointed to 5 to 6 constructs based on aggregates of the existing dimensions. The results were not satisfactory, so we performed an 8-factor confirmatory factor analysis, resulting in a good fit with only 1 item cross-loading between 2 dimensions. Cluster analysis showed that data from the READHY instrument can be clustered to create meaningful health technology readiness profiles of users. CONCLUSIONS: The 13 dimensions from heiQ, HLQ, and eHLQ can be used in combination to describe a user\u27s health technology readiness level and degree of enablement. Further studies in other populations are needed to understand whether the associations between dimensions are consistent and the number of dimensions can be reduced

Resting Metabolic Rate Does Not Change in Response to Different Types of Training in Subjects with Type 2 Diabetes

Background and objectivesAmbiguous results have been reported regarding the effects of training on resting metabolic rate (RMR), and the importance of training type and intensity is unclear. Moreover, studies in subjects with type 2 diabetes (T2D) are sparse. In this study, we evaluated the effects of interval and continuous training on RMR in subjects with T2D. Furthermore, we explored the determinants for training-induced alterations in RMR.MethodsData from two studies, both including T2D subjects, were encompassed in this manuscript. Study 1 was a randomized, crossover study where subjects (n = 14) completed three, 2-week interventions [control, continuous walking training (CWT), interval-walking training (IWT)] separated by washout periods. Training included 10 supervised treadmill sessions, 60 min/session. CWT was performed at moderate walking speed [aiming for 73% of walking peak oxygen uptake (VO2peak)], while IWT was performed as alternating 3-min repetitions at slow (54% VO2peak) and fast (89% VO2peak) walking speed. Study 2 was a single-arm training intervention study where subjects (n = 23) were prescribed 12 weeks of free-living IWT (at least 3 sessions/week, 30 min/session). Before and after interventions, RMR, physical fitness, body composition, and glycemic control parameters were assessed.ResultsNo overall intervention-induced changes in RMR were seen across the studies, but considerable inter-individual differences in RMR changes were seen in Study 2. At baseline, total body mass (TBM), fat-free mass (FFM), and fat mass were all associated with RMR. Changes in RMR were associated with changes in TBM and fat mass, and subjects who decreased body mass and fat mass also decreased their RMR. No associations were seen between changes in physical fitness, glycemic control, or FFM and changes in RMR.ConclusionNeither short-term continuous or interval-type training, nor longer term interval training affects RMR in subjects with T2D when no overall changes in body composition are seen. If training occurs concomitant with a reduction in fat mass, however, RMR is decreased.Clinical Trials Registration (www.ClinicalTrials.gov)NCT02320526 and NCT02089477

Criterion validity and reliability of a smartphone delivered sub-maximal fitness test for people with type 2 diabetes

BACKGROUND: Prevention of multi-morbidities following non-communicable diseases requires a systematic registration of adverse modifiable risk factors, including low physical fitness. The aim of the study was to establish criterion validity and reliability of a smartphone app (InterWalk) delivered fitness test in patients with type 2 diabetes. METHODS: Patients with type 2 diabetes (N = 27, mean (SD) age 64.2 (5.9) years, BMI 30.0 (5.1) kg/m(2), (30 % male)) completed a 7-min progressive walking protocol twice (with and without encouragement). VO(2) during the test was assessed using indirect calorimetry and the acceleration (vector magnitude) from the smartphone was obtained. The vector magnitude was used to predict VO(2peak) along with the co-variates weight, height and sex. The validity of the algorithm was tested when the smartphone was placed in the right pocket of the pants or jacket. The algorithm was validated using leave-one-out cross validation. Test-retest reliability was tested in a subset of participants (N = 10). RESULTS: The overall VO(2peak) prediction of the algorithm (R(2)) was 0.60 and 0.45 when the smartphone was placed in the pockets of the pants and jacket, respectively (p < 0.001). The mean bias (limits of agreement) in the cross validation was−0.4 (38) % (pants) and−0.1 (46) % (jacket). When the smartphone was placed in the jacket a significant intensity dependent bias (r = 0.5, p = 0.02) was observed. The test-retest intraclass correlations were 0.85 and 0.86 (p < 0.001), for the pants and jacket, respectively. No effects of encouragement were observed on test performance. CONCLUSION: In conclusion, the InterWalk Fitness Test is accurate and reliable for persons with type 2 diabetes when the smartphone is placed in the side pocket of the pants for. The test could give a fair estimate of the CRF in absence of a progressive maximal test during standardized conditions with the appropriate equipment. TRIAL REGISTRATION: www.clinicaltrials.org (NCT02089477), first registered (prospectively) on March 14th 201