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Self-management is crucial in the daily management of type 2 diabetes. It has been suggested that mHealth may be an important method for enhancing self-management when delivered in combination with health counseling.
The objective of this study was to test whether the use of a mobile phone–based self-management system used for 1 year, with or without telephone health counseling by a diabetes specialist nurse for the first 4 months, could improve glycated hemoglobin A1c (HbA1c) level, self-management, and health-related quality of life compared with usual care.
We conducted a 3-arm prospective randomized controlled trial involving 2 intervention groups and 1 control group. Eligible participants were persons with type 2 diabetes with an HbA1c level ≥7.1% (≥54.1 mmol/mol) and aged ≥18 years. Both intervention groups received the mobile phone–based self-management system Few Touch Application (FTA). The FTA consisted of a blood glucose–measuring system with automatic wireless data transfer, diet manual, physical activity registration, and management of personal goals, all recorded and operated using a diabetes diary app on the mobile phone. In addition, one intervention group received health counseling based on behavior change theory and delivered by a diabetes specialist nurse for the first 4 months after randomization. All groups received usual care by their general practitioner. The primary outcome was HbA1c level. Secondary outcomes were self-management (heiQ), health-related quality of life (SF-36), depressive symptoms (CES-D), and lifestyle changes (dietary habits and physical activity). Data were analyzed using univariate methods (
A total of 151 participants were randomized: 51 to the FTA group, 50 to the FTA-health counseling (FTA-HC) group, and 50 to the control group. Follow-up data after 1 year were available for 120 participants (79%). HbA1c level decreased in all groups, but did not differ between groups after 1 year. The mean change in the heiQ domain skills and technique acquisition was significantly greater in the FTA-HC group after adjusting for age, gender, and education (
The change in HbA1c level did not differ between groups after the 1-year intervention. Secondary outcomes did not differ between groups except for an increase in the self-management domain of skill and technique acquisition in the FTA-HC group. Older participants used the app more than the younger participants did.
Type 2 diabetes is a complex disease [
The field of technology-supported health care is growing and offers new ways of self-management education and support. Mobile phones are essential in people’s lives today and may serve as a platform for a variety of self-management tools, such as apps. However, the current reviews are inconclusive and the effects of mobile health (mHealth) remain unclear [
Apps for mHealth interventions are often combined with health counseling, but the research related to these complex interventions is inconclusive because of heterogeneity in the types of studies [
Few studies have used the combination of a mobile phone app for self-management supported by health counseling via telephone. Studies often include monitoring with real-time feedback from health care personnel, which may lead to the investigation of dimensions other than self-management. However, an intervention based largely on the patient’s initiative to self-manage at a frequency that does not interfere with daily life should be feasible in today’s society [
Earlier reviews noted the lack of integration of behavior change theory into mHealth research and recommended that interventions should be theory-based [
The current study is the Norwegian part of the European Union collaboration study RENEWING HEALTH (REgioNs of Europe WorkING together for HEALTH), which comprises telehealth interventions in different health care and home settings [
The aim of this study was to determine if the use of a mobile phone–based self-management system for 1 year, with or without telephone health counseling by a diabetes specialist nurse for the first 4 months, could improve HbA1c level, self-management, and health-related quality of life compared with usual care. The primary outcome was glycemic control, as assessed by the HbA1c level. Secondary outcomes were self-management and health-related quality of life, depressive symptoms, and lifestyle changes (dietary habits and physical activity).
We conducted a 3-armed prospective randomized controlled trial (RCT) with a 1:1:1 allocation ratio using block randomization to 1 of 2 intervention groups or to a control group. The allocation has been described in detail elsewhere [
All participants lived in their homes and received usual care by their general practitioner (GP). They were eligible if they were aged ≥18 years, had an HbA1c level ≥7.1% (54.1 mmol/mol), and were capable of completing questionnaires in the Norwegian language. They also had to be cognitively able to participate and to use the system and devices provided, although prior familiarity with mobile phones was not necessary. The majority of participants were recruited through 2 study centers in the southern and northern parts of Norway in collaboration with their GPs. Some participants were recruited from local public health clinics in the municipalities, through diabetes courses held by the specialist health providers for those newly diagnosed with type 2 diabetes, and through advertisement in The Norwegian Diabetes Association’s media. The HbA1c level was set to HbA1c >7.0% (53 mmol/mol); that is, above the treatment target according to the Norwegian guidelines [
There were 3 assessment points: baseline (time of randomization) and at 4 and 12 months after randomization. For the follow-up assessment, participants were invited to meet with the research team for data collection (questionnaires). Those not able to attend the follow-up meetings were sent questionnaires and a prepaid envelope to be returned by mail to the study center. All patients were asked to visit their GP for measuring of their HbA1c level and weight at the same time (±14 days) after they had filled in the questionnaires.
The Norwegian study in RENEWING HEALTH was a 1-year intervention to increase self-management comprised of 3 intervention groups: the Few Touch Application (FTA) intervention group, the FTA with health counseling (FTA-HC) intervention group, and the control group [
All participants in the 3 groups received usual care by their GP according to national guidelines [
The participants randomized to the control group received usual care [
In addition to usual care, these participants received a mobile phone with the FTA self-management system. The FTA system provided the user with a diabetes diary app designed to increase self-management through awareness, overview of relevant factors, and motivational feedback through symbols such as smiling faces and color codes in the app [
In addition to the mobile phone, FTA system, and usual care, the participants in the FTA-HC group received health counseling for the first 4 months of the project period. The health counseling was based on the transtheoretical model of stages of change [
A diabetes specialist nurse delivered the health counseling. She had special training and additional education in diabetes, was supervised by a clinical psychologist, and received support from a dietician when needed. Diet is an important element in the app. The nurse used a client-centered style for enhancing behavior change by helping the patients to explore and resolve ambivalence related to aspects of self-management. We provided a low-intensity intervention with a short counseling duration with few contacts between the patient and health counselor [
The participants were recruited to the project because of an HbA1c above the national recommendations (HbA1c>7.0%, 53 mmol/mol) [
Use of the FTA system in GP consultations was an option for the intervention groups; however, the participants had to take the initiative.
Demographic information were self-reported and included age, gender, education, employment status, and cohabitation (including those married and those living with a partner), and are described in detail elsewhere [
Clinical characteristics included HbA1c, weight, BMI, blood pressure, diabetes duration, comorbidities, complications, medication treatment, hypoglycemia, self-monitoring, and lifestyle variables (smoking, diet, and physical activity). Data were obtained from the GPs or self-reported (diabetes duration, comorbidity, hypoglycemia, self-monitoring, and lifestyle). Of these, only HbA1c and weight were collected at the 1-year follow-up.
Change in HbA1c level after 1 year was chosen as the primary outcome because it is the main target measure when treating diabetes and is frequently used when evaluating interventions [
The Health Education Impact Questionnaire (heiQ) [
To evaluate lifestyle and lifestyle changes, we investigated the participants’ dietary habits including recommended food items and traditional Norwegian dietary habits [
Registrations of the use of the FTA system were collected continuously through automatic data transfer to a secure server and into a usage log. For the FTA-HTC group, further education on usage of the app was supported by the diabetes specialist nurse. A dichotomous variable of substantial or not substantial use of the FTA was made retrospectively based on the usage log. To be categorized as a substantial user, the participant had to be an active user for at least 6 months. An active user was defined as one who had performed ≥5 blood glucose measurements during each of these 6 months and who had ≥50 interactions in the parts of the diary not including collection of data (eg, viewing data or accessing general information).
An a priori power calculation indicated that 34 participants in each of the 3 groups would be sufficient to detect significant changes in the primary outcome HbA1c level with an effect size of .35, a significance level of 5%, a standard deviation (SD) of the outcome variable of 0.5, statistical power of 80%, and a 2-tailed significance test. The sample was set to 50 in each of the 3 groups to allow for dropouts and 151 participants were included in total.
Block randomization was performed through the Center of Randomization at the Unit for Applied Clinical Research at the Norwegian University of Science and Technology in Trondheim using the Web Case Report Form.
The Regional Ethics Committee South East approved the protocol and all participants provided written informed consent before randomization.
The study could not be blinded for the participants or GPs and health providers because of the nature of the intervention, which required overt participation [
The baseline characteristics are reported as mean and SD (continuous variables) and counts and percentages (categorical variables). Data not available were considered to be missing and the results were based on intention-to-treat. Baseline differences between groups were assessed with 1-way ANOVA (continuous measurements) and chi square tests (categorical data). Within-group changes were analyzed using Student
Self-management with the FTA supported by health counseling.
Through the recruitment period, 298 persons were assessed for eligibility; 134 persons were not included, 52 did not wish to participate, and 82 did not meet the eligibility criteria (
Inclusion and randomization started in March 2011 and ended in September 2012. The first complete participant dataset was finalized in April 2012 and the follow-up data was finalized in October 2013.
After the 1-year follow-up, there was a total dropout attrition rate of 21% (31/151), with an equal distribution in the groups. Baseline analysis revealed no difference between those lost to follow-up and those who completed the study for all variables. For the primary outcome (HbA1c level), data were obtained for a total of 120 participants after the 1-year follow-up: 39 in the FTA group (dropout attrition 24%, 12/51), 40 in the FTA-HC group (dropout attrition 20%, 10/50), and 41 in the control group (dropout attrition 18%, 9/50). For the secondary self-reported outcomes, data were included from 119 participants, 38 in the FTA group, 40 in the FTA-HC group, and 41 in the control group.
Flowchart of enrollment.
The demographic and clinical baseline characteristics of the participants have been described in detail elsewhere [
Demographic and clinical characteristics at the baseline (N=151).
Characteristics | N | FTA |
FTA-HC |
Control group (n=50) | ||
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Age (years), mean (SD) | 151 | 58.6 (11.8) | 57.4 (12.1) | 55.9 (12.2) | |
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Gender (female), n (%) | 151 | 17 (33) | 25 (50) | 20 (40) | |
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151 |
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<12 years |
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26 (51) | 26 (52) | 31 (62) |
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12 years |
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4 (8) | 10 (20) | 3 (6) |
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>12 years |
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21 (41) | 14 (28) | 16 (32) |
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148 |
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Employed |
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22 (44) | 31 (63) | 26 (53) |
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Unemployed |
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13 (26) | 11 (22) | 17 (35) |
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Retired |
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15 (30) | 7 (14) | 6 (12) |
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Cohabitation status (cohabiting),c n (%) | 151 | 37 (73) | 36 (72) | 37 (74) | |
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HbA1c (%), mean (SD) | 151 | 8.1 (1.1) | 8.2 (1.1) | 8.3 (1.2) |
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HbA1c (mmol/mol), mean (SD)) | 151 | 65 (12.0) | 66 (12.0) | 67 (13.1) |
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HbA1c (%), median (range) | 151 | 7.8 (7.1-12.4) | 7.9 (7.1-11.3) | 7.9 (7.1-11.6) |
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HbA1c (mmol/mol), median (range) | 151 | 62 (54-112) | 63 (54-100) | 63 (54-103) |
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Weight (kg), mean (SD) | 132 | 98 (23.1) | 91 (20.3) | 96 (25) | |
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BMI kg/m2, mean (SD) | 129 | 32.4 (6.5) | 30.7 (5.6) | 32.0 (6.0) | |
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Systolic blood pressure (mmHg), mean (SD) | 121 | 136 (17.9) | 132 (13.7) | 134 (14.5) | |
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Duration of diabetes (years), mean (SD) | 138 | 11.2 (7.3) | 9.6 (8.4) | 9.4 (5.5) | |
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151 |
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0 |
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6 (12) | 8 (16) | 10 (20) |
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1-2 |
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33 (65) | 32 (64) | 32 (64) |
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≥3 |
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12 (23) | 10 (20) | 8 (16) |
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Late complication: foot ulcer, n (%) | 151 | 11 (22) | 8 (16) | 4 (8) | |
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Late complication: eye, n (%) | 151 | 7 (14) | 3 (6) | 9 (18) | |
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131 |
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Diet only |
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3 (7) | 2 (4) | 4 (11) |
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Oral agents only |
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20 (44) | 27 (57) | 16 (42) |
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Injections onlyd |
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9 (20) | 7 (15) | 3 (8) |
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Combination of oral agents and injections |
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14 (30) | 11 (23) | 15 (40) |
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Hypoglycemia (self-reported), n (%) | 148 | 23 (46) | 19 (39) | 27 (55) | |
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Self-monitoring blood glucose, n (%) | 151 | 48 (94) | 45 (90) | 49 (98) | |
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Smoking (yes) | 151 | 5 (10) | 12 (24) | 7 (14) | |
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Physical activity (physically active)e | 149 | 18 (37) | 16 (32) | 17 (34) | |
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Daily servings of fruit and vegetables | 148 | 2.8 (1.6) | 2.9 (1.7) | 3.8 (2.7) | |
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Poultry >3 servings per month | 146 | 33 (67) | 26 (52) | 28 (60) | |
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Meat >3 servings per month | 143 | 44 (88) | 44 (92) | 41 (91) | |
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Fish >3 servings per month | 148 | 41 (82) | 38 (78) | 37 (76) |
a Education: some high school or less (<12 years), high school graduate (12 years), or some college or more (>12 years).
b Employment status: employed (state employee, private employee, self-employed, or employed part-time); unemployed (student, military duty, homemaker, unemployed, or unable to work); and retired.
c Cohabitation status: living alone (not married, divorced, separated, or widowed); and cohabiting (married or living with someone).
d Injections were both insulin and other blood glucose–lowering injections.
e Physically active: those with >60 min per week at an intensity of “being short of breath” or higher intensity.
The change in HbA1c level did not differ significantly between the 3 groups after 1 year. However, HbA1c level declined within all groups and none of the participants in any of the groups reached their pretest levels at the 1-year follow-up (
Adjusting for age, gender, and educational level did not affect the change in HbA1c level nor did inclusion of possible confounders, such as changes in medication (glucose-lowering agents), BMI, depressive symptoms (CES-D), diabetes duration, and comorbidities (
Mean HbA1c levels (95% CI) at baseline and 1-year follow-up (N=119).
Mean HbA1c level, body weight, and heiQ domains at baseline and 1-year follow-up, and changes for those with 2 measurements.
Variables by group | n | Baseline, mean (95% CI) | 1-year follow-up, mean (95% CI) | Change, mean (95% CI) | |
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FTA | 39 | 8.1 (7.72, 8.53) | 7.8 (7.48, 8.15) | –0.31 (–0.67, 0.05) |
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FTA-HC | 40 | 8.1 (7.76, 8.43) | 8.0 (7.49, 8.41) | –0.15 (–0.58, 0.29) |
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Control | 41 | 8.4 (7.97, 8.76) | 8.2 (7.77, 8.61) | –0.16 (–0.50, 0.18) |
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FTA | 39 | 65 (61,70) | 62 (58,66) | –3.4 (–7.4,0.6) |
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FTA-HC | 40 | 65 (61,69) | 63 (58,68) | –1.6 (–6.3,3.1) |
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Control | 41 | 68 (64,72) | 66 (62,71) | –1.7 (–5.4,2.0) |
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FTA | 33 | 96.3 (87.99, 104.64) | 95.0 (87.54, 103.22) | –1.3 (–3.05, 0.43) |
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FTA-HC | 34 | 89.7 (82.45, 96.90) | 88.9 (82.28, 95.67) | –0.7 (–2.29, 0.84) |
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Control | 36 | 94.3 (85.31, 103.22) | 93.0 (84.44, 101.36) | –1.2 (–2.75, 0.54) |
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FTA | 38 | 3.23 (3.08, 3.38) | 3.19 (3.04, 3.34) | –0.04 (–0.18, 0.09) |
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FTA-HC | 40 | 3.20 (3.08, 3.31) | 3.22 (3.08, 3.36) | 0.02 (–0.15, 0.19) |
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Control | 41 | 3.12 (2.95, 3.29) | 3.09 (2.94, 3.24) | –0.03 (–0.19, 0.13) |
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FTA | 38 | 2.78 (2.52, 3.04) | 2.82 (2.60, 3.05) | 0.04 (–0.16, 0.25) |
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FTA-HC | 40 | 2.78 (2.57, 2.99) | 2.81 (2.57, 3.04) | 0.03 (–0.16, 0.21) |
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Control | 41 | 2.71 (2.51, 2.92) | 2.81 (2.58, 3.04) | 0.10 (–0.08, 0.27) |
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FTA | 38 | 2.92 (2.79, 3.04) | 2.88 (2.69, 3.06) | –0.04 (–0.20, 0.12) |
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FTA-HC | 40 | 2.89 (2.75, 3.02) | 3.08 (2.96, 3.21) | 0.19 (0.05, 0.33)a |
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Control | 41 | 2.95 (2.83, 3.06) | 2.94 (2.77, 3.12) | –0.01 (–0.14, 0.13) |
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FTA | 38 | 3.17 (2.98, 3.36) | 3.13 (3.00, 3.26) | –0.04 (–0.21, 0.13) |
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FTA-HC | 40 | 3.23 (3.09, 3.38) | 3.33 (3.19, 3.47) | 0.10 (–0.02, 0.21) |
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Control | 41 | 3.19 (3.02, 3.36) | 3.19 (3.02, 3.36) | 0.00 (–0.13, 0.13) |
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FTA | 38 | 3.06 (2.95, 3.15) | 3.09 (2.98, 3.19) | 0.04 (–0.07, 0.15) |
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FTA-HC | 40 | 3.09 (2.99, 3.18) | 3.18 (3.06, 3.30) | 0.09 (–0.01, 0.19) |
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Control | 41 | 3.14 (3.03, 3.24) | 3.15 (3.02, 3.28) | 0.01 (–0.12, 0.13) |
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FTA | 38 | 3.14 (2.97, 3.31) | 3.03 (2.86, 3.20) | –0.11 (–0.25, 0.04) |
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FTA-HC | 40 | 3.06 (2.91, 3.20) | 3.14 (2.96, 3.31) | 0.08 (–0.03, 0.20) |
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Control | 41 | 3.16 (3.00, 3.33) | 3.27 (3.09, 3.44) | 0.11 (–0.05, 0.26) |
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FTA | 38 | 3.04 (2.87, 3.21) | 2.93 (2.77, 3.09) | –0.11 (–0.23, 0.02) |
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FTA-HC | 40 | 3.02 (2.86, 3.17) | 3.02 (2.86, 3.19) | 0.01 (–0.09, 0.11) |
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Control | 41 | 2.94 (2.74, 3.15) | 2.95 (2.74, 3.16) | 0.01 (–0.14, 0.16) |
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FTA | 38 | 2.99 (2.77, 3.20) | 2.98 (2.76, 3.20) | –0.01 (–0.16, 0.13) |
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FTA-HC | 40 | 2.99 (2.81, 3.17) | 3.04 (2.84, 3.25) | 0.05 (–0.12, 0.22) |
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Control | 41 | 2.81 (2.57, 3.05) | 2.87 (2.64, 3.11) | 0.07 (–0.11, 0.24) |
a Change was statistically significant (
Body weight was slightly reduced in all 3 groups at the 1-year follow-up, although not significant (
Changes in HbA1c level, skill and technique acquisition, and health service navigation for the intervention groups versus the control group, unadjusted and adjusted for age, gender, and educational level in multiple linear regression analysis.a
Group | Unadjusted B | 95% CI |
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Adjusted Ba | 95% CI |
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FTA | –0.15 | –0.68, 0.37 | .57 | –0.22 | –0.75, 0.32 | .42 |
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FTA-HC | 0.01 | –0.51, 0.53 | .97 | 0.01 | –0.52, 0.54 | .97 |
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Control (ref) |
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FTA | –1.7 | –7.4, 4.1 |
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–2.4 | –8.2, 3.5 | .42 |
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FTA-HC | 0.1 | –5.6, 5.8 |
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0.1 | –5.6, 5.9 | .97 |
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Control (ref) |
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FTA | –0.04 | –0.24, 0.16 | .71 | –0.03 | –0.22, 0.17 | .79 |
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FTA-HC | 0.20 | 0.004, 0.40 | .046 | 0.21 | 0.01, 0.40 | .04 |
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Control (ref) |
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FTA | –0.21 | –0.41,–0.02 | .03 | –0.19 | –0.38, 0.01 | .06 |
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FTA-HC | –0.02 | –0.21, 0.17 | .82 | –0.004 | –0.19, 0.19 | .97 |
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Control (ref) |
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a This table presents 3 final multiple linear regression models, all adjusted for age, gender, and education.
After adjusting for age, gender, and educational level, the mean change in skill and technique acquisition was still significantly higher in the FTA-HC group (B=0.21; 95% CI 0.01-0.40;
When analyzing the effect of depressive symptoms independently of group allocation, we found that those who reported depressive symptoms (CES-D score ≥16 at baseline, indicating more depressive symptoms) reported a higher change in heiQ than those who did not report such symptoms. Both analyses of change in heiQ after 1 year were adjusted for age and gender. In the domains of positive and active engagement in life, the results were B=0.24, (95% CI 0.01-0.46; (
There were no significant differences in any of the 8 subscales or in the 2 summary component scores of the SF-36 between the 3 groups at the 1-year follow-up in both the unadjusted and adjusted analyses. The change in depressive symptoms measured with the CES-D did not differ significantly between groups for the total score (continuous variable) or for the number/percentage of participants with a score greater than the cutoff of ≥16 both before and after adjustments.
There were no significant differences between the groups in self-reported levels of physical activity (inactive to active or opposite). The changes in the intake of fruits and vegetables, meat, chocolate, and fish after 1 year did not differ between the 3 groups (results not shown).
Of those randomized to the FTA group, 20 of 51 (39%) were categorized as substantial users. In the FTA-HC group, 17 of 50 (34%) used the FTA part of the intervention substantially, and all these people attended ≥4 health counseling sessions; 42 of 50 (84%) attended ≥4 sessions of health counseling regardless of their FTA use.
Analyses of substantial versus nonsubstantial users of only the FTA, regardless of the intervention groups, did not reveal any statistically significant differences between groups regarding SF-36, heiQ, or depressive symptoms (CES-D). However, participants aged ≥63 years were more likely to be substantial users of the app (OR 2.7; 95% CI 1.02-7.12;
No serious adverse clinical events were reported from enrollment to the 1-year follow-up. However, a few undesired technical events were reported, such as trouble with the Bluetooth pairing required for automatic transmission of data from the glucometer to the app in the mobile phone. This may have been stressful for those affected and has been shown to lead to less satisfaction and decreased use of the technology in a previous study [
Although HbA1c level declined in all groups, the change did not differ significantly between either of the intervention groups and the control group after 1 year. However, the mean HbA1c level did not increase to the baseline level in any of the 3 groups. We found no effects on secondary outcomes other than a significant positive change in self-management reflected by the skill and technique acquisition scale in the FTA-HC group. Interestingly, participants aged ≥63 years were more likely to use the app.
In this study, we conducted a low-intensity mHealth intervention based on self-management with a mobile app and with a health-counseling booster for the first 4 months in one of the intervention groups. Previous reviews have investigated follow-up and intervention duration, and have found a trend of decreasing intervention effect over time [
The finding that the FTA-HC intervention group tended to have a greater change in self-management, as shown by the increase in skill and technique acquisition, may mean that they had an increased ability to reduce their symptoms related to type 2 diabetes and to manage their health effectively, including greater skills for using technical aids. A lack of effect in the other domains of self-management could indicate that our intervention did not reach those at highest risk of a decline in health [
The HbA1c level is widely used for evaluation of interventions, but its relevance to self-management has been questioned in the past few years [
Interventions are often designed without sufficient knowledge about the target group and without a theoretical framework [
Lack of findings in many behavior change studies may also relate to a lack of key components in available apps for persons with type 2 diabetes. Apps should be designed in the context of the current guidelines for treatment of type 2 diabetes to increase self-management [
There are also other possible explanations for the lack of difference in the change in HbA1c levels between groups. A total 39% of participants were substantial users of the app during the 1-year follow-up. The lack of effects on predefined outcomes may also relate to low use of the FTA, partly caused by outdated technology at the end of the study. The actual use of a mHealth intervention may reflect the external validity better than does the rate of dropouts [
Traditionally, the RCT is the gold standard for clinical trials. In this study, we achieved successful randomization with no statistically significant differences between the 3 groups at baseline. Moreover, all patients were recruited from the primary health care system, which may increase the generalizability of our results [
Some of the results were unexpected, such as the increased use among the older participants (aged ≥63 years). In previous research, a lack of effect was attributed to a fear of technology with increasing age [
In summary, we have successfully conducted a low-intensity RCT to test a mobile diabetes self-management system with and without health counseling. There were no significant differences in the change in HbA1c between the intervention groups and the control group. Skill and technique acquisition increased in those who received health counseling in addition to the self-management app. This may be important to their daily self-management of diabetes. Our findings indicate that age may not hinder the use of technology, as suggested by earlier research, but further research is needed to confirm this finding.
body mass index
Center for Epidemiologic Studies Depression Scale
Few Touch Application
FTA with health counseling
general practitioner
glycated hemoglobin A1c
Health Education Impact Questionnaire
mobile health
REgioNs of Europe WorkING together for HEALTH
Short-Form 36v2 Health Survey
This project was funded by (1) the EU through the ICT Policy Support Programme as part of the Competitiveness and Innovation Framework Programme, (2) the Norwegian Research Council, (3) the Health Authorities of Northern Norway, (4) the Norwegian Centre of Integrated Care and Telemedicine at the University Hospital of North-Norway, (5) the Oslo and Akershus University College, (6) the Akershus University Hospital, and (7) the Norwegian Diabetes Association. The authors thank the participants; their GPs; the leaders at the local health care public clinics; the diabetes specialist nurse, Tone Singstad, for delivering the health counseling; the nutritionist, Elisabeth Elind; our technical support team, Erlend Bønes and Elisabeth Ellefsen Sjaaeng; and the software developer team at NST for facilitating the diabetes diary system and data management. We also thank the project manager in the Norwegian pilot, Astrid Grøttland, for support during the study period.
None declared.