Quantified Self in de huisartsenpraktijk

Quantified Self staat voor de zelfmetende mens. Het aantal mensen dat met zelf gegeneerde gezondheidsgegevens het zorgproces binnenwandelt gaat de komende jaren groeien. Verschillende soorten activity trackers en gezondheidsapplicaties voor de smartphone maken het relatief eenvoudig om persoonlijke gegevens te verzamelen over beweging, voeding, slaap, hartslag, menstruatiecyclus, etc. Steeds vaker zullen patiënten dit soort data meenemen naar de huisarts. Het is daarom raadzaam kennis te nemen van wat er zoal aan zelfmeettechnologie beschikbaar is en hoe het is gesteld met de kwaliteit, toepasbaarheid of zelfs generaliseerbaarheid van de data. In dit artikel lichten we de achtergrond van Quantified Self toe, zetten we dit in een breder perspectief van technologische ontwikkelingen en zullen we iets zeggen over de zin en onzin van zelfmetingen, waarbij de focus zal liggen op Quantified Self met betrekking tot gezondheid en levensstijl

Do activity monitors increase physical activity in adults with overweight or obesity? A systematic review and meta-analysis

Objective: To systematically assess contemporary knowledge regarding behavioral physical activity interventions including an activity monitor (BPAI+) in adults with overweight or obesity. Methods: PubMed/MEDLINE, Embase, CINAHL, PsycINFO, CENTRAL and PEDro were searched for eligible full text articles up to July 1st 2015. Studies eligible for inclusion were (randomized) controlled trials describing physical activity outcomes in adults with overweight or obesity. Methodological quality was independently assessed employing the Cochrane Collaboration's tool for risk of bias. PROSPERO registration: CRD42015024086. Results: Fourteen studies (1157 participants) were included for systematic review and eleven for meta-analysis. A positive trend in BPAI+ effects on several measures of physical activity was ascertained compared to both waitlist or usual care (WL/UC) and behavioral physical activity interventions without an activity monitor (BPAI-). No convincing evidence of BPAI+ effectiveness on weight loss was found compared to BPAI-. Conclusions: Behavioral physical activity interventions with an activity monitor increase physical activity in adults with overweight or obesity. Also, adding an activity monitor to behavioral physical activity interventions appears to increase the effect on physical activity, although current evidence has not yet provided conclusive evidence for its effectiveness

Reliability and validity of ten consumer activity trackers

Background: Activity trackers can potentially stimulate users to increase their physical activity behavior. The aim of this study was to examine the reliability and validity of ten consumer activity trackers for measuring step count in both laboratory and free-living conditions. Method: Healthy adult volunteers (n = 33) walked twice on a treadmill (4.8 km/h) for 30 min while wearing ten different activity trackers (i.e. Lumoback, Fitbit Flex, Jawbone Up, Nike+ Fuelband SE, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and Moves mobile application). In free-living conditions, 56 volunteers wore the same activity trackers for one working day. Test-retest reliability was analyzed with the Intraclass Correlation Coefficient (ICC). Validity was evaluated by comparing each tracker with the gold standard (Optogait system for laboratory and ActivPAL for free-living conditions), using paired samples t-tests, mean absolute percentage errors, correlations and Bland-Altman plots. Results: Test-retest analysis revealed high reliability for most trackers except for the Omron (ICC .14), Moves app (ICC .37) and Nike+ Fuelband (ICC .53). The mean absolute percentage errors of the trackers in laboratory and free-living conditions respectively, were: Lumoback (−0.2, −0.4), Fibit Flex (−5.7, 3.7), Jawbone Up (−1.0, 1.4), Nike+ Fuelband (−18, −24), Misfit Shine (0.2, 1.1), Withings Pulse (−0.5, −7.9), Fitbit Zip (−0.3, 1.2), Omron (2.5, −0.4), Digiwalker (−1.2, −5.9), and Moves app (9.6, −37.6). Bland-Altman plots demonstrated that the limits of agreement varied from 46 steps (Fitbit Zip) to 2422 steps (Nike+ Fuelband) in the laboratory condition, and 866 steps (Fitbit Zip) to 5150 steps (Moves app) in the free-living condition. Conclusion: The reliability and validity of most trackers for measuring step count is good. The Fitbit Zip is the most valid whereas the reliability and validity of the Nike+ Fuelband is low

Self-tracking of physical activity in people with type 2 diabetes:a randomized controlled trial

The purpose of this study was to determine the efficacy of an online self-tracking program on physical activity, glycated hemoglobin, and other health measures in patients with type 2 diabetes. Seventy-two patients with type 2 diabetes were randomly assigned to an intervention or control group. All participants received usual care. The intervention group received an activity tracker (Fitbit Zip) connected to an online lifestyle program. Physical activity was analyzed in average steps per day from week 0 until 12. Health outcome measurements occurred in both groups at baseline and after 13 weeks. Results indicated that the intervention group significantly increased physical activity with 1.5 +/- 3 days per week of engagement in 30 minutes of moderate-vigorous physical activity versus no increase in the control group (P = .047). Intervention participants increased activity with 1255 +/- 1500 steps per day compared to their baseline (P <.010). No significant differences were found in glycated hemoglobin A1c, with the intervention group decreasing -0.28% +/- 1.03% and the control group showing -0.0% +/- 0.69% (P = .206). Responders (56%, increasing minimally 1000 steps/d) had significantly decreased glycated hemoglobin compared with nonresponders (-0.69% +/- 1.18% vs 0.22% +/- 0.47%, respectively; P = .007). To improve effectiveness of eHealth programs, additional strategies are needed

The Role of Self-Regulation in the Effect of Self-Tracking of Physical Activity and Weight on BMI

Self-tracking of health may have positive effects on lifestyle behavior and weight loss; however, not much is known about the role of psychological processes in this effect. The purpose of this study was to assess to what extent a change in self-regulation capabilities can explain weight loss after 4 and 12 months of self-tracking physical activity and weight. An explorative cohort study was conducted with measurements at baseline (T0), 4 months (T1), and 12 months (T2). Healthy adult volunteers (N = 80) were included and provided with a digital weight scale and an activity tracker. Personal characteristics as well as the intention to change weight and physical activity were measured at T0. Self-regulation capabilities (goal orientation, self-direction, decision making, and impulse control) were measured with the Self-Regulation Questionnaire at T0, T1, and T2, together with body weight. At T0, all four dimensions of self-regulation were negatively related to BMI (p <.01). At T1, weight significantly declined compared to T0 (− 2.0 kg/− 0.64 kg/m2, p <.001). At T2, this weight loss was maintained (− 1.8 kg/− 0.57 kg/m2, p <.01). At T1, intention to lose weight, self-weighing frequency, and an increase in goal orientation explained weight loss. At T2, an increase in decision making explained weight loss. Incremental self-regulation capabilities may explain weight loss after engaging in self-tracking of physical activity and weight. Future research should focus on exploring effective ways to further enhance self-regulation when using self-tracking technology and to assess the impact of different types of self-regulation stimuli on weight loss

Digitaal programma ‘leef gezond met diabetes’:een combinatie van een online platform, activity tracker en smartphoneapp ter bevordering van een gezonde leefstijl bij mensen met diabetes

Zelf gegenereerde gezondheidsinformatie speelt steeds vaker een rol in het zorgproces. Deze trend komt enerzijds door het gemak waarmee mensen zelf met wearables en apps data over zichzelf kunnen verzamelen. Anderzijds wordt deze trend door zorgverleners en de overheid gestimuleerd om de klinische observaties met data uit de thuissituatie te kunnen verrijken, of omdat door reorganisatie of bezuiniging zelfmanagement is gewenst. Ondanks positieve verwachtingen is er nog weinig onderzoek gedaan naar de implementatie en effectiviteit van zelfmeting en hieraan gerelateerde producten en diensten

Do activity monitors increase physical activity in adults with overweight or obesity?:a systematic review and meta-analysis

Objective: To systematically assess contemporary knowledge regarding behavioral physical activity interventions including an activity monitor (BPAI+) in adults with overweight or obesity. Methods: PubMed/MEDLINE, Embase, CINAHL, PsycINFO, CENTRAL, and PEDro were searched for eligible full-text articles up to 1 July 2015. Studies eligible for inclusion were (randomized) controlled trials describing physical activity outcomes in adults with overweight or obesity. Methodological quality was independently assessed employing the Cochrane Collaboration's tool for risk of bias. Results: Fourteen studies (1,157 participants) were included for systematic review and 11 for meta-analysis. A positive trend in BPAI+ effects on several measures of physical activity was ascertained compared with both wait list or usual care and behavioral physical activity interventions without an activity monitor (BPAI−). No convincing evidence of BPAI+ effectiveness on weight loss was found compared with BPAI−. Conclusions: Behavioral physical activity interventions with an activity monitor increase physical activity in adults with overweight or obesity. Also, adding an activity monitor to behavioral physical activity interventions appears to increase the effect on physical activity, although current evidence has not yet provided conclusive evidence for its effectiveness