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| Name | Class |
|---|---|
| Federico II University | OTHER |
| University of Bergamo | OTHER |
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This study aims to evaluate the efficacy of a physical activity promotion intervention focused on walking behavior. The intervention is delivered via mobile application in a sample drawn from the healthy adult population.
A sample of sedentary adults will be invited to participate in a 30 days intervention delivered via a mobile app to achieve the goal of 7,000 daily steps.
Before and after the intervention, some crucial variables will be evaluated for the formation of the intention to change behavior and for the transition from intention to action (attitudes, subjective norms, self-efficacy, expectations related to change, risk perception, social support, planning) to compare two models of behavioral change.
During the intervention, messages will be sent daily, and the number of steps taken daily will be monitored. The aim is to compare the effectiveness of two types of communication in promoting a positive behavioral change: non-personalized communication centered on well-being (non-tailored communication) vs. personalized communication based on the psychological characteristics evaluated before the intervention (tailored communication). The physical activity carried out over 30 days by the participants who will receive the messages (tailored and non-tailored) will be compared with the physical activity carried out by participants who will not receive any messages (control group).
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Tailored messages (TM) | Experimental | Participants assigned to this arm receive a daily tailored message on the benefits of taking at least 7000 steps daily. Tailoring concerns change-related expectations, risk perception, planning, retention capacity, resilience, and coping skills and is based on the responses provided by participants at baseline evaluation. In addition, they receive a daily request to declare the number of steps taken (walking self-monitoring).In particular, every evening, the mobile application sends a message to the participants requesting to enter the number of steps taken during the day in a dedicated app section. |
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| Non tailored messages (NTM) | Experimental | Participants assigned to this arm receive a daily non-tailored message on the emotional benefits of taking at least 7000 steps daily. In addition, they receive a daily request to declare the number of steps taken (walking self-monitoring). In particular, every evening, the mobile application sends a message to the participants requesting to enter the number of steps taken during the day in a dedicated app section. |
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| No messages (NM) | No Intervention | Participants assigned to this arm receive a daily request to declare the number of steps taken (walking self-monitoring). In particular, every evening, the mobile application sends a message to the participants requesting to enter the number of steps taken during the day in a dedicated app section. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Walk behavior change: Tailored messages (TM) | Behavioral | Every afternoon at the same time, the mobile application sends a message to the participants of the TM arm. The message is tailored based on the answers provided to the pre-intervention questionnaire. An example message is: "you think you are not able to walk regularly when your morale is low: do not give up because physical activity is also good for the mood!" The intervention is provided for 30 days. |
| Measure | Description | Time Frame |
|---|---|---|
| Change from Baseline in Physical Activity | Physical activity is assessed with the International Physical Activity Questionnaire (IPAQ; Mannocci et al., 2010). The scale comprises seven items on Physical Activity providing information about time spent walking, moderate and vigorous intensity, and sedentary activity. The elements are structured to provide separate scores for walking, moderate and vigorous intensity activity, and a combined total score to describe the overall activity level. Data collected with IPAQ are reported as a continuous measure and reported as MET-median minutes. | Baseline and 30 days |
| Change from Baseline in Walk behavior | Walk behavior is self-monitored daily. Each evening, participants receive a message and enter the number of steps taken in a specific app section based on the data reported on the smartwatch or the smartphone's native app. The mean number of steps at the intervention's beginning and the end is then calculated. These two measures are compared to verify whether a statistically significant increase in daily steps is observed over time. | Baseline and 30 days |
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| Measure | Description | Time Frame |
|---|---|---|
| Adherence to a healthy lifestyle: diet | Regarding adherence to a healthy lifestyle, participants report the frequency of consumption of 17 different food types through a 7-point Likert scale. The items were taken from a survey of the National Institute of Statistics (https://www.istat.it/it/archivio/91926). We will classify each behavior as adequate (score = 1) or inadequate (score = 0), following international and national guidelines. We will add up the scores obtained for specific foods similar to the MedDietScore scale (Trichopoulou et al., 2003), in order to build a diet adequacy index for use in subsequent analyses. The higher the score, the higher the adherence to guidelines. |
Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Marco D'Addario, PhD | Contact | 0039 02 6448 3824 | marco.daddario@unimib.it | |
| Roberta Adorni, PhD | Contact | roberta.adorni1@unimib.it |
| Name | Affiliation | Role |
|---|---|---|
| Marco D'Addario, PhD | University of Milano Bicocca | Principal Investigator |
| Patrizia Steca, PhD | University of Milano Bicocca | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Milano-Bicocca | Recruiting | Milan | MI | 20126 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 33044542 | Background | Davis A, Sweigart R, Ellis R. A systematic review of tailored mHealth interventions for physical activity promotion among adults. Transl Behav Med. 2020 Oct 12;10(5):1221-1232. doi: 10.1093/tbm/ibz190. | |
| 34713097 | Background | Martin-Martin J, Roldan-Jimenez C, De-Torres I, Muro-Culebras A, Escriche-Escuder A, Gonzalez-Sanchez M, Ruiz-Munoz M, Mayoral-Cleries F, Biro A, Tang W, Nikolova B, Salvatore A, Cuesta-Vargas AI. Behavior Change Techniques and the Effects Associated With Digital Behavior Change Interventions in Sedentary Behavior in the Clinical Population: A Systematic Review. Front Digit Health. 2021 Jul 8;3:620383. doi: 10.3389/fdgth.2021.620383. eCollection 2021. |
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| ID | Term |
|---|---|
| D057185 | Sedentary Behavior |
| D009043 | Motor Activity |
| ID | Term |
|---|---|
| D001519 | Behavior |
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The baseline assessment, the intervention, and the post-intervention assessment are provided automatically via the mobile app. In addition, participants are randomly assigned to a specific experimental group before starting the study and do not know which experimental group they are assigned. These features of the study minimize observer bias.
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| Walk behavior change: Non tailored messages (NTM) | Behavioral | Every afternoon at the same time, the mobile application sends a message to the participants of the NTM arm. The message concerns the emotional well-being resulting from the performance of the physical activity and is not tailored. An example message is: "walking regularly in the fresh air improves your mood." The intervention is provided for 30 days. |
|
| Baseline |
| Adherence to a healthy lifestyle: alcohol consumption | Participants report the frequency of consumption of alcoholic beverages during the last month through a 6-point Likert scale (1 = almost every day; 6 = never). The higher the score, the higher the adherence to guidelines. | Baseline |
| Adherence to a healthy lifestyle: smoking | Participants report if they smoke, and the frequency of smoking through a 6-point Likert scale (1 = every day; 6 = never). The higher the score, the higher the adherence to guidelines. | Baseline |
| Adherence to a healthy lifestyle: medication | Medication adherence is evaluated using the brief Italian version of the Morisky Green Levine Scale (MGLS), a questionnaire consisting of 4 questions with dichotomous answers (yes or no). Each behavior is classified as adequate (score = 1) or inadequate (score = 0). The item responses are added up. The higher the score, the higher the medication adherence. A total score of <2 is indicative of poor adherence. | Baseline |
| Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): intention to change walking behavior | Participants indicate how much they intend to walk regularly (take at least 7,000 steps per day at a moderate speed) through a single item on a 7-point Likert scale, where 1 = totally disagree, 7 = totally agree. The higher the score, the higher the intention to walk regularly. | Baseline |
| Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): action self-efficacy | Participants indicate confidence in their abilities to walk regularly through a single item on a 5-point Likert scale, where 1 = not capable and 5 = fully capable. The higher the score, the higher the action self-efficacy. | Baseline |
| Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): health risk perception | Participants indicate how exposed they feel to health risks (six items) concerning their current unhealthy behavior (i.e., how little they walk) on a 7-point Likert scale, where 1 = in no way, 7 = very much. The score is calculated as the mean of the six items' scores. The higher the score, the higher the health risk perception. | Baseline |
| Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): planning | Participants indicate if they have a detailed plan concerning six concrete aspects of achieving the goal of 7,000 steps per day (for example, when and where to walk) on a 7-point Likert scale, where 1 = in no way, 7 = very much. The score is calculated as the mean of the six items' scores. The higher the score, the higher the planning. | Baseline |
| Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): outcome expectancies | Participants indicate their positive (3 items) and negative (3 items) expectancies about the health, emotional and social effects of taking at least 7,000 steps a day on a 7-point Likert scale, where 1 = in no way, 7 = very much. The score of each variable (positive expectancies and negative expectancies) is calculated as the mean of the three items' scores. The higher the score, the higher the expectancies. | Baseline |
| Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): maintenance self-efficacy | Participants indicate confidence in their ability to maintain the new healthier behavior despite obstacles and difficulties through ten items on a 7-point Likert scale, where 1 = in no way, 7 = very much. The score is calculated as the mean of the ten items' scores. The higher the score, the higher the maintenance self-efficacy. | Baseline |
| Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): recovery self-efficacy | Participants indicate confidence in their ability to regain healthy behavior if a lapse occurs through a single item on a 5-point Likert scale, where 1 = not capable and 5 = fully capable. The higher the score, the higher the maintenance self-efficacy. | Baseline |
| Predictors of behavioral change according to the Theory of Planned Behavior (TPB; Ajzen, 1991; Canfora et al., 2018): instrumental attitude | This variable is evaluated using a ten-item semantic differential on a 7-point scale, ranging from 1 (e.g., useless) to 7 (e.g., useful). Participants read each pair of adjectives and check a box more or less close to the adjective they feel is best suited to describe what it would mean to reach the goal of 7,000 steps daily. The score is calculated as the mean of the ten items' scores. The higher the score, the more positive the attitude. | Baseline |
| Predictors of behavioral change according to the Theory of Planned Behavior (TPB; Ajzen, 1991; Canfora et al., 2018): subjective norms | Participants indicate how significant others judge regular walking as important (subjective norms or the perceived social pressure concerning walking behavior). This variable is measured through five items on a 7-point Likert scale, where 1 = totally disagree, 7 = totally agree. The score is calculated as the mean of the five items' scores. The higher the score, the stronger the subjective norms. | Baseline |
| Predictors of behavioral change according to the Theory of Planned Behavior (TPB; Ajzen, 1991; Canfora et al., 2018): social support | Participants indicate how much support/approval they would receive from their partner, family, friends, and walking group/associations on a 5-point Likert scale, where 1 = no support and 5 = much support. The score is calculated as the mean of the four items' scores. The higher the score, the greater the perceived social support. | Baseline |
| Predictors of behavioral change according to the Theory of Planned Behavior (TPB; Ajzen, 1991; Canfora et al., 2018): anticipated affective reactions | Participants indicate to what extent they would experience positive affective reactions (in case of reaching the goal of 7,000 steps per day - 3 items) or negative affective reactions (in case of failure to reach the goal - 3 items) on a 7-point Likert scale, where 1 = totally disagree, 7 = totally agree. The score of each variable (positive affective reactions and negative affective reactions) is calculated as the mean of the three items' scores. The higher the score, the stronger the affective reaction. | Baseline |
| 34788584 | Background | Newsome A, Gilliard T, Phillips A, Dedrick R. Understanding the perceptions of sedentary college students' engagement in physical activity: application of the theory of planned behavior. J Am Coll Health. 2023 Dec;71(9):2813-2822. doi: 10.1080/07448481.2021.1998069. Epub 2021 Nov 17. |
| 30888321 | Background | Romeo A, Edney S, Plotnikoff R, Curtis R, Ryan J, Sanders I, Crozier A, Maher C. Can Smartphone Apps Increase Physical Activity? Systematic Review and Meta-Analysis. J Med Internet Res. 2019 Mar 19;21(3):e12053. doi: 10.2196/12053. |
| 29165018 | Background | Rowley TW, Lenz EK, Swartz AM, Miller NE, Maeda H, Strath SJ. Efficacy of an Individually Tailored, Internet-Mediated Physical Activity Intervention in Older Adults: A Randomized Controlled Trial. J Appl Gerontol. 2019 Jul;38(7):1011-1022. doi: 10.1177/0733464817735396. Epub 2017 Oct 25. |
| 28049344 | Background | Steca P, Pancani L, Cesana F, Fattirolli F, Giannattasio C, Greco A, D'Addario M, Monzani D, Cappelletti ER, Magrin ME, Miglioretti M, Sarini M, Scrignaro M, Vecchio L, Franzelli C. Changes in physical activity among coronary and hypertensive patients: A longitudinal study using the Health Action Process Approach. Psychol Health. 2017 Mar;32(3):361-380. doi: 10.1080/08870446.2016.1273353. Epub 2017 Jan 4. |
| 14715035 | Background | Tudor-Locke C, Bassett DR Jr. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med. 2004;34(1):1-8. doi: 10.2165/00007256-200434010-00001. |
| 30973747 | Background | Zhang CQ, Zhang R, Schwarzer R, Hagger MS. A meta-analysis of the health action process approach. Health Psychol. 2019 Jul;38(7):623-637. doi: 10.1037/hea0000728. Epub 2019 Apr 11. |
| 30121311 | Background | Carfora V, Caso D, Palumbo F, Conner M. Promoting water intake. The persuasiveness of a messaging intervention based on anticipated negative affective reactions and self-monitoring. Appetite. 2018 Nov 1;130:236-246. doi: 10.1016/j.appet.2018.08.017. Epub 2018 Aug 16. |
| 3945130 | Background | Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986 Jan;24(1):67-74. doi: 10.1097/00005650-198601000-00007. |
| Background | Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 1991; 50(2): 179-211. |
| Background | Mannocci A, Di Thiene D, Del Cimmuto A, Masala D, Boccia A, De Vito E, La Torre G. International Physical Activity Questionnaire: validation and assessment in an Italian sample. Italian Journal of Public Health. 2012; 7(4) |
| Background | Schwarzer R. Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology. 2008; 57(1): 1-29 |
| 40676042 | Derived | Adorni R, Zanatta F, Serino S, Vanutelli ME, Caso D, D'Addario M, Steca P. Efficacy of a theory-based and tailored mHealth intervention promoting walking behavior: a preliminary randomized controlled trial. Sci Rep. 2025 Jul 18;15(1):26033. doi: 10.1038/s41598-025-09634-3. |