Computerised decision support in physical activity interventions: A systematic literature review

Triantafyllidis, Andreas; Filos, Dimitrios; Claes, Jomme; Buys, Roselien; Cornelissen, Véronique; Kouidi, Evangelia; Chouvarda, Ioanna; Maglaveras, Nicos

doi:10.1016/j.ijmedinf.2017.12.012

Cited by 13 publications

(12 citation statements)

References 53 publications

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“…With the above in mind, the employment of wearable devices would enable to realize the development of reliable and unobtrusive computerized systems and digital health interventions for children and their carers, and overcome the potential inaccuracy of children's self-reports. Such an approach can be a step towards acquiring optimal physical activity outcomes through longitudinal physical activity monitoring, assessment, and coaching [25].…”

Section: Discussionmentioning

confidence: 99%

Comparison between Self-Reported and Accelerometer-Measured Physical Activity in Young versus Older Children

Triantafyllidis

Alexiadis

Soutos

et al. 2021

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Physical inactivity in children is a major public health challenge, for which valid physical activity assessment tools are needed. Wearable devices provide a means for objective assessment of children’s physical activity, but they are often not adopted because of issues such as cost, comfort, and privacy. In this context, self-reporting tools could be employed, but their validity in relation to a child’s age is understudied. We present the agreement of one of the most popular self-reporting tools, the Physical Activity Questionnaire for Children (PAQ-C) with accelerometer-measured physical activity in 9-year-old versus 12-year-old children, wearing an accelerometer-based wearable device for seven consecutive days. We study the relationship between the PAQ-C and accelerometer scores using Spearman’s rank correlation coefficients and Bland–Altman plots in a sample of 131 children included for analysis. Overall, there was correlation between PAQ-C score and physical activity measures for the 12-year-old children (rho = 0.47 for total physical activity, rho = 0.43 for moderate-to-vigorous physical activity, rho = 0.41 for steps, p < 0.01), but not for the 9-year-old children (rho = 0.08 for total physical activity, rho = 0.21 for moderate-to-vigorous physical activity, rho = 0.19 for steps, p > 0.05). All PAQ-C items other than item 3 (activity at recess) did not reach significance in correlation with accelerometry for the 9-year-old children (p > 0.05). Therefore, the use of wearable devices for more objective assessment of physical activity in younger children should be preferred.

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Section: Discussionmentioning

confidence: 99%

Comparison between Self-Reported and Accelerometer-Measured Physical Activity in Young versus Older Children

Triantafyllidis

Alexiadis

Soutos

et al. 2021

Digital

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“…A solid foundation of evidence demonstrates shorter-term effectiveness of internet-mediated interventions [ 7 - 17 ], particularly when combined with wearable PA sensors, tailored motivational messaging, and coaching [ 18 - 20 ]. By leveraging the broad availability of mobile sensors, we can increase access to interventions aimed at increasing PA levels [ 21 - 23 ]. The evidence-base for mobile health (mHealth) interventions, however, is largely based on small trials with short-term follow-up [ 13 , 21 ]; longer-term engagement with mHealth programs is not always sustained [ 24 - 26 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of Adding Telephone-Based Brief Coaching to an mHealth App (Stay Strong) for Promoting Physical Activity Among Veterans: Randomized Controlled Trial

et al. 2020

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Background Though maintaining physical conditioning and a healthy weight are requirements of active military duty, many US veterans lose conditioning and rapidly gain weight after discharge from active duty service. Mobile health (mHealth) interventions using wearable devices are appealing to users and can be effective especially with personalized coaching support. We developed Stay Strong, a mobile app tailored to US veterans, to promote physical activity using a wrist-worn physical activity tracker, a Bluetooth-enabled scale, and an app-based dashboard. We tested whether adding personalized coaching components (Stay Strong+Coaching) would improve physical activity compared to Stay Strong alone. Objective The goal of this study is to compare 12-month outcomes from Stay Strong alone versus Stay Strong+Coaching. Methods Participants (n=357) were recruited from a national random sample of US veterans of recent wars and randomly assigned to the Stay Strong app alone (n=179) or Stay Strong+Coaching (n=178); both programs lasted 12 months. Personalized coaching components for Stay Strong+Coaching comprised of automated in-app motivational messages (3 per week), telephone-based human health coaching (up to 3 calls), and personalized weekly goal setting. All aspects of the enrollment process and program delivery were accomplished virtually for both groups, except for the telephone-based coaching. The primary outcome was change in physical activity at 12 months postbaseline, measured by average weekly Active Minutes, captured by the Fitbit Charge 2 device. Secondary outcomes included changes in step counts, weight, and patient activation. Results The average age of participants was 39.8 (SD 8.7) years, and 25.2% (90/357) were female. Active Minutes decreased from baseline to 12 months for both groups (P<.001) with no between-group differences at 6 months (P=.82) or 12 months (P=.98). However, at 12 months, many participants in both groups did not record Active Minutes, leading to missing data in 67.0% (120/179) for Stay Strong and 61.8% (110/178) for Stay Strong+Coaching. Average baseline weight for participants in Stay Strong and Stay Strong+Coaching was 214 lbs and 198 lbs, respectively, with no difference at baseline (P=.54) or at 6 months (P=.28) or 12 months (P=.18) postbaseline based on administrative weights, which had lower rates of missing data. Changes in the number of steps recorded and patient activation also did not differ by arm. Conclusions Adding personalized health coaching comprised of in-app automated messages, up to 3 coaching calls, plus automated weekly personalized goals, did not improve levels of physical activity compared to using a smartphone app alone. Physical activity in both groups decreased over time. Sustaining long-term adherence and engagement in this mHealth intervention proved difficult; approximately two-thirds of the trial’s 357 participants failed to sync their Fitbit device at 12 months and, thus, were lost to follow-up. Trial Registration ClinicalTrials.gov NCT02360293; https://clinicaltrials.gov/ct2/show/NCT02360293 International Registered Report Identifier (IRRID) RR2-10.2196/12526

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“…A solid foundation of evidence demonstrates shorter-term effectiveness of internet-mediated interventions [7][8][9][10][11][12][13][14][15][16][17], particularly when combined with wearable PA sensors, tailored motivational messaging, and coaching [18][19][20]. By leveraging the broad availability of mobile sensors, we can increase access to interventions aimed at increasing PA levels [21][22][23]. The evidence-base for mobile health (mHealth) interventions, however, is largely based on small trials with short-term follow-up [13,21]; longer-term engagement with mHealth programs is not always sustained [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Effect of Adding Telephone-Based Brief Coaching to an mHealth App (Stay Strong) for Promoting Physical Activity Among Veterans: Randomized Controlled Trial (Preprint)

Damschroder¹,

Buis²,

McCant³

et al. 2020

Preprint

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BACKGROUND Though maintaining physical conditioning and a healthy weight are requirements of active military duty, many US veterans lose conditioning and rapidly gain weight after discharge from active duty service. Mobile health (mHealth) interventions using wearable devices are appealing to users and can be effective especially with personalized coaching support. We developed Stay Strong, a mobile app tailored to US veterans, to promote physical activity using a wrist-worn physical activity tracker, a Bluetooth-enabled scale, and an app-based dashboard. We tested whether adding personalized coaching components (Stay Strong+Coaching) would improve physical activity compared to Stay Strong alone. OBJECTIVE The goal of this study is to compare 12-month outcomes from Stay Strong alone versus Stay Strong+Coaching. METHODS Participants (n=357) were recruited from a national random sample of US veterans of recent wars and randomly assigned to the Stay Strong app alone (n=179) or Stay Strong+Coaching (n=178); both programs lasted 12 months. Personalized coaching components for Stay Strong+Coaching comprised of automated in-app motivational messages (3 per week), telephone-based human health coaching (up to 3 calls), and personalized weekly goal setting. All aspects of the enrollment process and program delivery were accomplished virtually for both groups, except for the telephone-based coaching. The primary outcome was change in physical activity at 12 months postbaseline, measured by average weekly Active Minutes, captured by the Fitbit Charge 2 device. Secondary outcomes included changes in step counts, weight, and patient activation. RESULTS The average age of participants was 39.8 (SD 8.7) years, and 25.2% (90/357) were female. Active Minutes decreased from baseline to 12 months for both groups (P<.001) with no between-group differences at 6 months (P=.82) or 12 months (P=.98). However, at 12 months, many participants in both groups did not record Active Minutes, leading to missing data in 67.0% (120/179) for Stay Strong and 61.8% (110/178) for Stay Strong+Coaching. Average baseline weight for participants in Stay Strong and Stay Strong+Coaching was 214 lbs and 198 lbs, respectively, with no difference at baseline (P=.54) or at 6 months (P=.28) or 12 months (P=.18) postbaseline based on administrative weights, which had lower rates of missing data. Changes in the number of steps recorded and patient activation also did not differ by arm. CONCLUSIONS Adding personalized health coaching comprised of in-app automated messages, up to 3 coaching calls, plus automated weekly personalized goals, did not improve levels of physical activity compared to using a smartphone app alone. Physical activity in both groups decreased over time. Sustaining long-term adherence and engagement in this mHealth intervention proved difficult; approximately two-thirds of the trial’s 357 participants failed to sync their Fitbit device at 12 months and, thus, were lost to follow-up. CLINICALTRIAL ClinicalTrials.gov NCT02360293; https://clinicaltrials.gov/ct2/show/NCT02360293 INTERNATIONAL REGISTERED REPORT RR2-10.2196/12526

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Computerised decision support in physical activity interventions: A systematic literature review

Cited by 13 publications

References 53 publications

Comparison between Self-Reported and Accelerometer-Measured Physical Activity in Young versus Older Children

Comparison between Self-Reported and Accelerometer-Measured Physical Activity in Young versus Older Children

Effect of Adding Telephone-Based Brief Coaching to an mHealth App (Stay Strong) for Promoting Physical Activity Among Veterans: Randomized Controlled Trial

Effect of Adding Telephone-Based Brief Coaching to an mHealth App (Stay Strong) for Promoting Physical Activity Among Veterans: Randomized Controlled Trial (Preprint)

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