scholarly journals Estimating Measurement Error of the Patient Activation Measure for Respondents with Partially Missing Data

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Ariel Linden
Keyword(s):  
Missing Data ◽  
Measurement Error ◽  
Controlled Trial ◽  
Patient Activation ◽  
Percentage Error ◽  
Missing Responses ◽  
Improve Measurement ◽  
Patient Activation Measure ◽  
Minimum Score ◽  
Missing Items

The patient activation measure (PAM) is an increasingly popular instrument used as the basis for interventions to improve patient engagement and as an outcome measure to assess intervention effect. However, a PAM score may be calculated when there are missing responses, which could lead to substantial measurement error. In this paper, measurement error is systematically estimated across the full possible range of missing items (one to twelve), using simulation in which populated items were randomly replaced with missing data for each of 1,138 complete surveys obtained in a randomized controlled trial. The PAM score was then calculated, followed by comparisons of overall simulated average mean, minimum, and maximum PAM scores to the true PAM score in order to assess the absolute percentage error (APE) for each comparison. With only one missing item, the average APE was 2.5% comparing the true PAM score to the simulated minimum score and 4.3% compared to the simulated maximum score. APEs increased with additional missing items, such that surveys with 12 missing items had average APEs of 29.7% (minimum) and 44.4% (maximum). Several suggestions and alternative approaches are offered that could be pursued to improve measurement accuracy when responses are missing.

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

10.2196/19216 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e19216
Author(s):  
Laura J Damschroder ◽  
Lorraine R Buis ◽  
Felicia A McCant ◽  
Hyungjin Myra Kim ◽  
Richard Evans ◽  
...  
Keyword(s):  
Physical Activity ◽  
Missing Data ◽  
Controlled Trial ◽  
Patient Activation ◽  
Health Coaching ◽  
Mobile App ◽  
Physical Conditioning ◽  
Healthy Weight ◽  
Mhealth Intervention ◽  
Us Veterans

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

scholarly journals The effect of tablet computers with a mobile patient portal application on hospitalized patients’ knowledge and activation

2015 ◽  
Vol 23 (1) ◽  
pp. 159-165 ◽  
Author(s):  
Kevin J O’Leary ◽  
Mary E Lohman ◽  
Eckford Culver ◽  
Audrey Killarney ◽  
G Randy Smith ◽  
...  
Keyword(s):  
Short Form ◽  
Controlled Trial ◽  
Patient Activation ◽  
Control Unit ◽  
Hospitalized Patients ◽  
Patient Portal ◽  
Tablet Computers ◽  
Patient Activation Measure ◽  
Physician Role ◽  
Intervention Unit

Abstract Objective To assess the effect of tablet computers with a mobile patient portal application on hospitalized patients’ knowledge and activation. Methods We developed a mobile patient portal application including pictures, names, and role descriptions of team members, scheduled tests and procedures, and a list of active medications. We evaluated the effect of the application using a controlled trial involving 2 similar units in a large teaching hospital. Patients on the intervention unit were offered use of tablet computers with the portal application during their hospitalization. We assessed patients’ ability to correctly name their nurse, primary service physicians, physician roles, planned tests and procedures, medications started, and medications stopped since admission. We also administered the Short Form of the Patient Activation Measure. Results Overall, 100 intervention- and 102 control-unit patients participated. A higher percentage of intervention-unit patients correctly named ≥1 physician (56% vs 29.4%; P &lt; .001) and ≥1 physician role (47% vs 15.7%; P &lt; .001). Knowledge of nurses’ names, planned tests, planned procedures, and medication changes was generally low and not significantly different between the study units. The Short Form of the Patient Activation Measure mean (SD) score was also not significantly different at 64.1 (13.4) vs 62.7 (12.8); P = .46. Conclusions Additional research is needed to identify optimal methods to engage and inform patients during their hospitalization, which will improve preparation for self- management after discharge.

A randomized controlled study: The effects of self-referral to inpatient treatment on patient activation

2017 ◽  
Vol 41 (S1) ◽  
pp. S381-S381
Author(s):  
I.E.O. Moljord ◽  
M. Lara-Cabrera
Keyword(s):  
Mental Health ◽  
Inpatient Treatment ◽  
Controlled Trial ◽  
Patient Activation ◽  
User Participation ◽  
Self Report ◽  
Controlled Study ◽  
Preliminary Results ◽  
Randomized Controlled ◽  
Patient Activation Measure

IntroductionSelf-referral to inpatient treatment (SRIT) has recently been implemented in Norway in several community mental health centers (CMHC) in an effort to increase activation and to improve access to mental health services and timely treatment.ObjectiveTo examine the effect of having a contract for self-referral to inpatient treatment (SRIT) in patients with severe mental disorders. This intervention was based on personalized care planning, legislation regarding patients’ rights and is intended to enhance user participation.AimsTo assess the 12-month effect on patient activation measure-13 (PAM-13).MethodsA randomized controlled trial with 53 adult patients; 26 participants got a SRIT contract which they could use to refer themselves into a CMHC up to five days for each referral without contacting a doctor in advance. Preliminary results on the primary outcome after 12 months with the self-report questionnaires Patient Activation Measure (PAM-13), will be analyzed using linear mixed and regression models.ResultsThe preliminary results showed no significant effect on PAM-13 (estimated mean difference [emd] −0.41, 95% CI [CI]: −7.49 to 6.67). A post hoc analysis found an effect of SRIT on PAM-13 in those with baseline PAM-13 scores below ≤ 47.ConclusionThere were no group differences.Trial designClinicaltrials.gov NCT01133587.Disclosure of interestThe authors have not supplied their declaration of competing interest.

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

2020 ◽  
Author(s):  
Laura J Damschroder ◽  
Lorraine R Buis ◽  
Felicia A McCant ◽  
Hyungjin Myra Kim ◽  
Richard Evans ◽  
...  
Keyword(s):  
Physical Activity ◽  
Missing Data ◽  
Controlled Trial ◽  
Patient Activation ◽  
Health Coaching ◽  
Mobile App ◽  
Physical Conditioning ◽  
Healthy Weight ◽  
Mhealth Intervention ◽  
Us Veterans

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 <i>Stay Strong</i>, 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 (<i>Stay Strong+Coaching</i>) would improve physical activity compared to <i>Stay Strong</i> alone. OBJECTIVE The goal of this study is to compare 12-month outcomes from <i>Stay Strong</i> alone versus <i>Stay Strong+Coaching.</i> METHODS Participants (n=357) were recruited from a national random sample of US veterans of recent wars and randomly assigned to the <i>Stay Strong</i> app alone (n=179) or <i>Stay Strong+Coaching</i> (n=178); both programs lasted 12 months. Personalized coaching components for <i>Stay Strong+Coaching</i> 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 (<i>P</i>&lt;.001) with no between-group differences at 6 months (<i>P</i>=.82) or 12 months (<i>P</i>=.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 <i>Stay Strong</i> and 61.8% (110/178) for <i>Stay Strong+Coaching</i>. Average baseline weight for participants in <i>Stay Stron</i>g and <i>Stay Strong+Coaching</i> was 214 lbs and 198 lbs, respectively, with no difference at baseline (<i>P</i>=.54) or at 6 months (<i>P</i>=.28) or 12 months (<i>P</i>=.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

1214-P: Assessing the Efficacy of Home-Based Kidney Care on Patient Activation Measure (PAM) among Patients with Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1214-P
Author(s):  
VALLABH SHAH ◽  
VERNON S. PANKRATZ ◽  
DONICA M. GHAHATE ◽  
JEANETTE BOBELU ◽  
ROBERT NELSON
Keyword(s):  
Patient Activation ◽  
Home Based ◽  
Patient Activation Measure ◽  
Patients With Diabetes

Examining Nonnormal Latent Variable Distributions for Non-Ignorable Missing Data

2021 ◽  
Vol 45 (3) ◽  
pp. 159-177
Author(s):  
Chen-Wei Liu
Keyword(s):  
Missing Data ◽  
Latent Variable ◽  
Bivariate Normal Distribution ◽  
Parameter Estimates ◽  
Missing Responses ◽  
Variable Distribution ◽  
Item Responses ◽  
Nonnormal Distribution ◽  
The Impact ◽  
Biased Estimates

Missing not at random (MNAR) modeling for non-ignorable missing responses usually assumes that the latent variable distribution is a bivariate normal distribution. Such an assumption is rarely verified and often employed as a standard in practice. Recent studies for “complete” item responses (i.e., no missing data) have shown that ignoring the nonnormal distribution of a unidimensional latent variable, especially skewed or bimodal, can yield biased estimates and misleading conclusion. However, dealing with the bivariate nonnormal latent variable distribution with present MNAR data has not been looked into. This article proposes to extend unidimensional empirical histogram and Davidian curve methods to simultaneously deal with nonnormal latent variable distribution and MNAR data. A simulation study is carried out to demonstrate the consequence of ignoring bivariate nonnormal distribution on parameter estimates, followed by an empirical analysis of “don’t know” item responses. The results presented in this article show that examining the assumption of bivariate nonnormal latent variable distribution should be considered as a routine for MNAR data to minimize the impact of nonnormality on parameter estimates.

scholarly journals Patient Activation, Depressive Symptoms, and Self-Rated Health: Care Management Intervention Effects among High-Need, Medically Complex Adults

2021 ◽  
Vol 18 (11) ◽  
pp. 5690
Author(s):  
Cynthia F. Corbett ◽  
Kenn B. Daratha ◽  
Sterling McPherson ◽  
Crystal L. Smith ◽  
Michael S. Wiser ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Care Management ◽  
Controlled Trial ◽  
Patient Activation ◽  
Study Groups ◽  
Telephone Intervention ◽  
Management Intervention ◽  
Self Rated Health ◽  
Randomized Controlled ◽  
Complex Population

The purpose of this randomized controlled trial (n = 268) at a Federally Qualified Health Center was to evaluate the outcomes of a care management intervention versus an attention control telephone intervention on changes in patient activation, depressive symptoms and self-rated health among a population of high-need, medically complex adults. Both groups had similar, statistically significant improvements in patient activation and self-rated health. Both groups had significant reductions in depressive symptoms over time; however, the group who received the care management intervention had greater reductions in depressive symptoms. Participants in both study groups who had more depressive symptoms had lower activation at baseline and throughout the 12 month study. Findings suggest that patients in the high-need, medically complex population can realize improvements in patient activation, depressive symptoms, and health status perceptions even with a brief telephone intervention. The importance of treating depressive symptoms in patients with complex health conditions is highlighted.

scholarly journals Evaluation of the EQ-5D-3L and 5L versions in low back pain patients

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
A. M. Garratt ◽  
H. Furunes ◽  
C. Hellum ◽  
T. Solberg ◽  
J. I. Brox ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Missing Data ◽  
Measurement Properties ◽  
Low Back ◽  
Improve Measurement ◽  
Multicenter Randomized Controlled Trial ◽  
Patient Reported ◽  
Ceiling Effects

Abstract Background The EuroQol EQ-5D is one of the most widely researched and applied patient-reported outcome measures worldwide. The original EQ-5D-3L and more recent EQ-5D-5L include three and five response categories respectively. Evidence from healthy and sick populations shows that the additional two response categories improve measurement properties but there has not been a concurrent comparison of the two versions in patients with low back pain (LBP). Methods LBP patients taking part in a multicenter randomized controlled trial of lumbar total disc replacement and conservative treatment completed the EQ-5D-3L and 5L in an eight-year follow-up questionnaire. The 3L and 5L were assessed for aspects of data quality including missing data, floor and ceiling effects, response consistency, and based on a priori hypotheses, associations with the Oswestry Disability Index (ODI), Pain-Visual Analogue Scales and Hopkins Symptom Checklist (HSCL-25). Results At the eight-year follow-up, 151 (87%) patients were available and 146 completed both the 3L and 5L. Levels of missing data were the same for the two versions. Compared to the EQ-5D-5L, the 3L had significantly higher floor (pain discomfort) and ceiling effects (mobility, self-care, pain/discomfort, anxiety/depression). For these patients the EQ-5D-5L described 73 health states compared to 28 for the 3L. Shannon’s indices showed the 5L outperformed the 3L in tests of classification efficiency. Correlations with the ODI, Pain-VAS and HSCL-25 were largely as hypothesized, the 5L having slightly higher correlations than the 3L. Conclusion The EQ-5D assesses important aspect of health in LBP patients and the 5L improves upon the 3L in this respect. The EQ-5D-5L is recommended in preference to the 3L version, however, further testing in other back pain populations together with additional measurement properties, including responsiveness to change, is recommended. Trial registration: retrospectively registered: https://clinicaltrials.gov/ct2/show/NCT01704677.

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